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Kreuzthaler M, Pfeifer B, Schulz S. Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2025; 2024:620-627. [PMID: 40417589 PMCID: PMC12099355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/27/2025]
Abstract
Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific natural language processing services. In this work, we have analyzed a clinical problem list table consisting of millions of ICD-10 codes assigned to short problem list descriptions in German. We have investigated whether the given data forms a valuable resource within a secondary use case scenario for coding support. Our proposed methodology exploits an embedding-based k-NN classifier, which was evaluated based on its coding performance, leveraging the multilingual BERT based language model SapBERT-UMLS in comparison with medBERT.de, which is specifically tailored to medical and clinical language resources in German. Our approach reached a weighted F1-measure of 0.87 using SapBERT-UMLS and an F1-measure of 0.86 for medBERT.de. The approach revealed promising coding results when reusing annotated language resources out of clinical routine documentation.
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Affiliation(s)
- Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
| | - Bastian Pfeifer
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
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2
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Wang Y, Wang F, Liu W, Geng Y, Shi Y, Tian Y, Zhang B, Luo Y, Sun X. New drug discovery and development from natural products: Advances and strategies. Pharmacol Ther 2024; 264:108752. [PMID: 39557343 DOI: 10.1016/j.pharmthera.2024.108752] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 11/06/2024] [Accepted: 11/08/2024] [Indexed: 11/20/2024]
Abstract
Natural products (NPs) have a long history as sources for drug discovery, more than half of approved drugs are related to NPs, which also exhibit multifaceted advantages in the clinical treatment of complex diseases. However, bioactivity screening of NPs, target identification, and design optimization require continuously improved strategies, the complexity of drug mechanism of action and the limitations of technological strategies pose numerous challenges to the development of new drugs. This review begins with an overview of bioactivity- and target-based drug development patterns for NPs, advances in NP screening and derivatization, and the advantages and problems of major targets such as genes and proteins. Then, target-based drugs as well as identification and validation methods are further discussed to elucidate their mechanism of action. Subsequently, the current status and development trend of the application of traditional and emerging technologies in drug discovery and development of NPs are systematically described. Finally, the collaborative strategy of multi-technology integration and multi-disciplinary intersection is emphasized for the challenges faced in the identification, optimization, activity evaluation, and clinical application of NPs. It is hoped to provide a systematic overview and inspiration for exploring new drugs from natural resources in the future.
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Affiliation(s)
- Yixin Wang
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Fan Wang
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Wenxiu Liu
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Yifei Geng
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Yahong Shi
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Yu Tian
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China
| | - Bin Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China.
| | - Yun Luo
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China.
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100193, China; Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, China; Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, China.
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3
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He X, Tang Y, Yu B, Li S, Ren Y. Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:2064-2075. [PMID: 39137086 DOI: 10.1109/tcbb.2024.3442199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction. To avoid redundant information, we design dynamic path planning strategy for argument detection. To take full use of the information between the trigger identification and argument detection subtasks, and reduce the cascading errors, we build a joint event extraction model. Experimental results demonstrate our approach achieves the best F-score on the biomedical benchmark MLEE dataset and outperforms the recent state-of-the-art methods.
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Zong H, Wu R, Cha J, Feng W, Wu E, Li J, Shao A, Tao L, Li Z, Tang B, Shen B. Advancing Chinese biomedical text mining with community challenges. J Biomed Inform 2024; 157:104716. [PMID: 39197732 DOI: 10.1016/j.jbi.2024.104716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 08/22/2024] [Accepted: 08/25/2024] [Indexed: 09/01/2024]
Abstract
OBJECTIVE This study aims to review the recent advances in community challenges for biomedical text mining in China. METHODS We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. RESULTS We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. CONCLUSION Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.
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Affiliation(s)
- Hui Zong
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Rongrong Wu
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiaxue Cha
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Weizhe Feng
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Erman Wu
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiakun Li
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China; Department of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Aibin Shao
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liang Tao
- Faculty of Business Information, Shanghai Business School, Shanghai 201400, China
| | | | - Buzhou Tang
- Department of Computer Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
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Varela-Vega A, Posada-Reyes AB, Méndez-Cruz CF. Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach. Database (Oxford) 2024; 2024:baae094. [PMID: 39213391 PMCID: PMC11363960 DOI: 10.1093/database/baae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 08/09/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024]
Abstract
Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.
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Affiliation(s)
- Alfredo Varela-Vega
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, UNAM, Av. Universidad S/N Col. Chamilpa, Cuernavaca, Morelos 62210, México
| | - Ali-Berenice Posada-Reyes
- Laboratorio de Microbiología, Inmunología y Salud Pública, Facultad de Estudios Superiores Cuautitlán, UNAM, Carretera Cuautitlán-Teoloyucan Km. 2.5, Xhala, Cuautitlán Izcalli, Estado de México 54714, México
| | - Carlos-Francisco Méndez-Cruz
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, UNAM, Av. Universidad S/N Col. Chamilpa, Cuernavaca, Morelos 62210, México
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6
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Jonker RAA, Almeida T, Antunes R, Almeida JR, Matos S. Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes. Database (Oxford) 2024; 2024:baae068. [PMID: 39083461 PMCID: PMC11290360 DOI: 10.1093/database/baae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/15/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024]
Abstract
The identification of medical concepts from clinical narratives has a large interest in the biomedical scientific community due to its importance in treatment improvements or drug development research. Biomedical named entity recognition (NER) in clinical texts is crucial for automated information extraction, facilitating patient record analysis, drug development, and medical research. Traditional approaches often focus on single-class NER tasks, yet recent advancements emphasize the necessity of addressing multi-class scenarios, particularly in complex biomedical domains. This paper proposes a strategy to integrate a multi-head conditional random field (CRF) classifier for multi-class NER in Spanish clinical documents. Our methodology overcomes overlapping entity instances of different types, a common challenge in traditional NER methodologies, by using a multi-head CRF model. This architecture enhances computational efficiency and ensures scalability for multi-class NER tasks, maintaining high performance. By combining four diverse datasets, SympTEMIST, MedProcNER, DisTEMIST, and PharmaCoNER, we expand the scope of NER to encompass five classes: symptoms, procedures, diseases, chemicals, and proteins. To the best of our knowledge, these datasets combined create the largest Spanish multi-class dataset focusing on biomedical entity recognition and linking for clinical notes, which is important to train a biomedical model in Spanish. We also provide entity linking to the multi-lingual Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) vocabulary, with the eventual goal of performing biomedical relation extraction. Through experimentation and evaluation of Spanish clinical documents, our strategy provides competitive results against single-class NER models. For NER, our system achieves a combined micro-averaged F1-score of 78.73, with clinical mentions normalized to SNOMED CT with an end-to-end F1-score of 54.51. The code to run our system is publicly available at https://github.com/ieeta-pt/Multi-Head-CRF. Database URL: https://github.com/ieeta-pt/Multi-Head-CRF.
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Affiliation(s)
- Richard A A Jonker
- IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - Tiago Almeida
- IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - Rui Antunes
- IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - João R Almeida
- IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - Sérgio Matos
- IEETA/DETI, LASI, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
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7
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Taub-Tabib H, Shamay Y, Shlain M, Pinhasov M, Polak M, Tiktinsky A, Rahamimov S, Bareket D, Eyal B, Kassis M, Goldberg Y, Kaminski Rosenberg T, Vulfsons S, Ben Sasson M. Identifying symptom etiologies using syntactic patterns and large language models. Sci Rep 2024; 14:16190. [PMID: 39003296 PMCID: PMC11246441 DOI: 10.1038/s41598-024-65645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/21/2024] [Indexed: 07/15/2024] Open
Abstract
Differential diagnosis is a crucial aspect of medical practice, as it guides clinicians to accurate diagnoses and effective treatment plans. Traditional resources, such as medical books and services like UpToDate, are constrained by manual curation, potentially missing out on novel or less common findings. This paper introduces and analyzes two novel methods to mine etiologies from scientific literature. The first method employs a traditional Natural Language Processing (NLP) approach based on syntactic patterns. By using a novel application of human-guided pattern bootstrapping patterns are derived quickly, and symptom etiologies are extracted with significant coverage. The second method utilizes generative models, specifically GPT-4, coupled with a fact verification pipeline, marking a pioneering application of generative techniques in etiology extraction. Analyzing this second method shows that while it is highly precise, it offers lesser coverage compared to the syntactic approach. Importantly, combining both methodologies yields synergistic outcomes, enhancing the depth and reliability of etiology mining.
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Affiliation(s)
| | - Yosi Shamay
- Faculty of Biomedical Engineering, Technion, Haifa, Israel
| | | | | | | | | | | | | | - Ben Eyal
- Allen Institute for AI, Seattle, USA
| | | | - Yoav Goldberg
- Allen Institute for AI, Seattle, USA
- Computer Science Department, Bar Ilan University, Ramat Gan, Israel
| | | | - Simon Vulfsons
- Institute for Pain Medicine, Rambam Health Campus, Haifa, Israel
| | - Maayan Ben Sasson
- Institute for Pain Medicine, Rambam Health Campus, Haifa, Israel.
- Alan Edwards Pain Management Unit, McGill University Health Centre, Montreal, QC, Canada.
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Vlieger R, Austin D, Apthorp D, Daskalaki E, Lensky A, Walton-Sonda D, Suominen H, Lueck CJ. The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review. Brain Res 2024; 1832:148827. [PMID: 38403040 DOI: 10.1016/j.brainres.2024.148827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.
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Affiliation(s)
- Robin Vlieger
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.
| | - Duncan Austin
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Deborah Apthorp
- School of Psychology, Faculty of Medicine and Health, University of New England, Armidale, NSW, Australia; School of Computing, College of Engineering, Computing & Cybernetics, Australian National University, Canberra, ACT, Australia
| | - Elena Daskalaki
- School of Computing, College of Engineering, Computing & Cybernetics, Australian National University, Canberra, ACT, Australia
| | - Artem Lensky
- School of Engineering and Technology, University of New South Wales, Canberra, ACT, Australia; School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Sydney, NSW, Australia
| | - Dianne Walton-Sonda
- The ACT Health Library and Multimedia Service, The Canberra Hospital, Canberra, ACT, Australia
| | - Hanna Suominen
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia; School of Computing, College of Engineering, Computing & Cybernetics, Australian National University, Canberra, ACT, Australia; Department of Computing, University of Turku, Turku, Finland
| | - Christian J Lueck
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia
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Nachtegael C, De Stefani J, Cnudde A, Lenaerts T. DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations. Database (Oxford) 2024; 2024:baae039. [PMID: 38805753 PMCID: PMC11131422 DOI: 10.1093/database/baae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/17/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic variant relations, despite the reports in literature that epistatic effects between combinations of variants in different loci (or genes) are important to understand disease etiologies. This work presents the creation of a unique dataset of oligogenic variant combinations, geared to train tools to help in the curation of scientific literature. To overcome the hurdles associated with the number of unlabelled instances and the cost of expertise, active learning (AL) was used to optimize the annotation, thus getting assistance in finding the most informative subset of samples to label. By pre-annotating 85 full-text articles containing the relevant relations from the Oligogenic Diseases Database (OLIDA) with PubTator, text fragments featuring potential digenic variant combinations, i.e. gene-variant-gene-variant, were extracted. The resulting fragments of texts were annotated with ALAMBIC, an AL-based annotation platform. The resulting dataset, called DUVEL, is used to fine-tune four state-of-the-art biomedical language models: BiomedBERT, BiomedBERT-large, BioLinkBERT and BioM-BERT. More than 500 000 text fragments were considered for annotation, finally resulting in a dataset with 8442 fragments, 794 of them being positive instances, covering 95% of the original annotated articles. When applied to gene-variant pair detection, BiomedBERT-large achieves the highest F1 score (0.84) after fine-tuning, demonstrating significant improvement compared to the non-fine-tuned model, underlining the relevance of the DUVEL dataset. This study shows how AL may play an important role in the creation of bioRE dataset relevant for biomedical curation applications. DUVEL provides a unique biomedical corpus focusing on 4-ary relations between two genes and two variants. It is made freely available for research on GitHub and Hugging Face. Database URL: https://huggingface.co/datasets/cnachteg/duvel or https://doi.org/10.57967/hf/1571.
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Affiliation(s)
- Charlotte Nachtegael
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Boulevard du Triomphe, CP 263, Brussels 1050, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe, CP 212, Brussels 1050, Belgium
| | - Jacopo De Stefani
- Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe, CP 212, Brussels 1050, Belgium
| | - Anthony Cnudde
- Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe, CP 212, Brussels 1050, Belgium
- Pharmacologie, Pharmacothérapie et Suivi Pharmaceutique, Université Libre de Bruxelles, Boulevard du Triomphe, CP 205, Brussels 1050, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Boulevard du Triomphe, CP 263, Brussels 1050, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe, CP 212, Brussels 1050, Belgium
- Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
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10
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Irrera O, Marchesin S, Silvello G. MetaTron: advancing biomedical annotation empowering relation annotation and collaboration. BMC Bioinformatics 2024; 25:112. [PMID: 38486137 PMCID: PMC10941452 DOI: 10.1186/s12859-024-05730-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. RESULTS We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. CONCLUSIONS MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.
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Affiliation(s)
- Ornella Irrera
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Stefano Marchesin
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Padua, Italy
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11
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Lou Y, Zhu X, Tan K. Dictionary-based matching graph network for biomedical named entity recognition. Sci Rep 2023; 13:21667. [PMID: 38066007 PMCID: PMC10709457 DOI: 10.1038/s41598-023-48564-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER. Biomedical dictionaries, implemented through a masked manner, are frequently employed in these methods to enhance entity recognition. However, their performance remains limited. In this work, we propose a dictionary-based matching graph network for BioNER. This approach utilizes the matching graph method to project all possible dictionary-based entity combinations in the text onto a directional graph. The network is implemented coherently with a bi-directional graph convolutional network (BiGCN) that incorporates the matching graph information. Our proposed approach fully leverages the dictionary-based matching graph instead of a simple masked manner. We have conducted numerous experiments on five typical Bio-NER datasets. The proposed model shows significant improvements in F1 score compared to the state-of-the-art (SOTA) models: 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA. The results show that our model, which is superior to other models, can effectively recognize natural biomedical named entities.
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Affiliation(s)
- Yinxia Lou
- School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China
| | - Xun Zhu
- School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China.
| | - Kai Tan
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
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12
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Berge GT, Granmo OC, Tveit TO, Ruthjersen AL, Sharma J. Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records. BMC Med Inform Decis Mak 2023; 23:188. [PMID: 37723446 PMCID: PMC10507898 DOI: 10.1186/s12911-023-02271-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/17/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency. METHODS In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refines the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classification. RESULTS In empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method's performance on the task of classification (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks. CONCLUSIONS Based on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP.
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Affiliation(s)
- Geir Thore Berge
- Department of Information Systems, University of Agder, Kristiansand, Norway
- Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway
| | | | - Tor Oddbjørn Tveit
- Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway
- Department of Anesthesia and Intensive Care, Sørlandet Hospital Trust, Kristiansand, Norway
| | - Anna Linda Ruthjersen
- Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway
| | - Jivitesh Sharma
- Department of Technology and eHealth, Sørlandet Hospital Trust, Kristiansand, Norway.
- Department of ICT, University of Agder, Grimstad, Norway.
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13
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Faessler E, Hahn U, Schäuble S. GePI: large-scale text mining, customized retrieval and flexible filtering of gene/protein interactions. Nucleic Acids Res 2023:7177881. [PMID: 37224532 DOI: 10.1093/nar/gkad445] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/01/2023] [Accepted: 05/11/2023] [Indexed: 05/26/2023] Open
Abstract
We present GePI, a novel Web server for large-scale text mining of molecular interactions from the scientific biomedical literature. GePI leverages natural language processing techniques to identify genes and related entities, interactions between those entities and biomolecular events involving them. GePI supports rapid retrieval of interactions based on powerful search options to contextualize queries targeting (lists of) genes of interest. Contextualization is enabled by full-text filters constraining the search for interactions to either sentences or paragraphs, with or without pre-defined gene lists. Our knowledge graph is updated several times a week ensuring the most recent information to be available at all times. The result page provides an overview of the outcome of a search, with accompanying interaction statistics and visualizations. A table (downloadable in Excel format) gives direct access to the retrieved interaction pairs, together with information about the molecular entities, the factual certainty of the interactions (as verbatim expressed by the authors), and a text snippet from the original document that verbalizes each interaction. In summary, our Web application offers free, easy-to-use, and up-to-date monitoring of gene and protein interaction information, in company with flexible query formulation and filtering options. GePI is available at https://gepi.coling.uni-jena.de/.
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Affiliation(s)
- Erik Faessler
- Jena University Language and Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, Fürstengraben 30, 07743 Jena, Germany
| | - Udo Hahn
- Jena University Language and Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, Fürstengraben 30, 07743 Jena, Germany
| | - Sascha Schäuble
- Jena University Language and Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, Fürstengraben 30, 07743 Jena, Germany
- Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745 Jena, Germany
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14
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Valentin S, Decoupes R, Lancelot R, Roche M. Animal disease surveillance: How to represent textual data for classifying epidemiological information. Prev Vet Med 2023; 216:105932. [PMID: 37247579 DOI: 10.1016/j.prevetmed.2023.105932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/07/2023] [Accepted: 05/10/2023] [Indexed: 05/31/2023]
Abstract
The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the early warning and preparedness context is recognized. This study evaluates machine learning methods for classifying information from animal disease-related news at a fine-grained level (i.e., epidemiological topic). We compare two textual representations, the bag-of-words method and a distributional approach, i.e., word embeddings. Both representations performed well for binary relevance classification (F-measure of 0.839 and 0.871, respectively). Bag-of-words representation was outperformed by word embedding representation for classifying sentences into fine-grained epidemiological topics (F-measure of 0.745). Our results suggest that the word embedding approach is of interest in the context of low-frequency classes in a specialized domain. However, this representation did not bring significant performance improvements for binary relevance classification, indicating that the textual representation should be adapted to each classification task.
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Affiliation(s)
- Sarah Valentin
- CIRAD, F-34398 Montpellier, France; ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier, France; TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France; Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Rémy Decoupes
- TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
| | - Renaud Lancelot
- CIRAD, F-34398 Montpellier, France; ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier, France
| | - Mathieu Roche
- CIRAD, F-34398 Montpellier, France; TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France.
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15
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Kreuzthaler M, Brochhausen M, Zayas C, Blobel B, Schulz S. Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems. Front Med (Lausanne) 2023; 10:1073313. [PMID: 37007792 PMCID: PMC10050682 DOI: 10.3389/fmed.2023.1073313] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/13/2023] [Indexed: 03/17/2023] Open
Abstract
This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.
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Affiliation(s)
- Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Cilia Zayas
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Bernd Blobel
- Medical Faculty, University of Regensburg, Regensburg, Germany
- eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Deggendorf, Germany
- First Medical Faculty, Charles University Prague, Prague, Czechia
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- Averbis GmbH, Freiburg, Germany
- *Correspondence: Stefan Schulz,
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16
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Valentin S, Arsevska E, Vilain A, De Waele V, Lancelot R, Roche M. Elaboration of a new framework for fine-grained epidemiological annotation. Sci Data 2022; 9:655. [PMID: 36289243 PMCID: PMC9606314 DOI: 10.1038/s41597-022-01743-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 10/03/2022] [Indexed: 11/09/2022] Open
Abstract
Event-based surveillance (EBS) gathers information from a variety of data sources, including online news articles. Unlike the data from formal reporting, the EBS data are not structured, and their interpretation can overwhelm epidemic intelligence (EI) capacities in terms of available human resources. Therefore, diverse EBS systems that automatically process (all or part of) the acquired nonstructured data from online news articles have been developed. These EBS systems (e.g., GPHIN, HealthMap, MedISys, ProMED, PADI-web) can use annotated data to improve the surveillance systems. This paper describes a framework for the annotation of epidemiological information in animal disease-related news articles. We provide annotation guidelines that are generic and applicable to both animal and zoonotic infectious diseases, regardless of the pathogen involved or its mode of transmission (e.g., vector-borne, airborne, by contact). The framework relies on the successive annotation of all the sentences from a news article. The annotator evaluates the sentences in a specific epidemiological context, corresponding to the publication date of the news article.
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Affiliation(s)
- Sarah Valentin
- grid.121334.60000 0001 2097 0141UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), University of Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France ,grid.121334.60000 0001 2097 0141UMR ASTRE (Unit for Animals, Health, Territories, Risks and Ecosystems), University of Montpellier, CIRAD, INRAE, Montpellier, France ,grid.86715.3d0000 0000 9064 6198Department of Biology, University of Sherbrooke, Sherbrooke, Canada ,grid.14709.3b0000 0004 1936 8649Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada
| | - Elena Arsevska
- grid.121334.60000 0001 2097 0141UMR ASTRE (Unit for Animals, Health, Territories, Risks and Ecosystems), University of Montpellier, CIRAD, INRAE, Montpellier, France ,grid.8183.20000 0001 2153 9871French Agricultural Research for Development (CIRAD), Montpellier, France
| | - Aline Vilain
- grid.508031.fVeterinary Epidemiology Service, Departement of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Valérie De Waele
- Department of Environmental and Agricultural Studies, Public Service of Wallonia, B5030 Gembloux, Belgium
| | - Renaud Lancelot
- grid.121334.60000 0001 2097 0141UMR ASTRE (Unit for Animals, Health, Territories, Risks and Ecosystems), University of Montpellier, CIRAD, INRAE, Montpellier, France ,grid.8183.20000 0001 2153 9871French Agricultural Research for Development (CIRAD), Montpellier, France
| | - Mathieu Roche
- grid.121334.60000 0001 2097 0141UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), University of Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France ,grid.8183.20000 0001 2153 9871French Agricultural Research for Development (CIRAD), Montpellier, France
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17
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Hossain MZ, Daskalaki E, Brüstle A, Desborough J, Lueck CJ, Suominen H. The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review. BMC Med Inform Decis Mak 2022; 22:242. [PMID: 36109726 PMCID: PMC9476596 DOI: 10.1186/s12911-022-01985-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. METHODS Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. RESULTS Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.
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Affiliation(s)
- Md Zakir Hossain
- School of Computing, College of Engineering and Computer Science, Australian National University, Canberra, ACT Australia
| | - Elena Daskalaki
- School of Computing, College of Engineering and Computer Science, Australian National University, Canberra, ACT Australia
| | - Anne Brüstle
- The John Curtin School of Medical Research, College of Health and Medicine, Australian National University, Canberra, ACT Australia
| | - Jane Desborough
- Department of Health Services Research and Policy, Research School of Population Health, College of Health and Medicine, Australian National University, Canberra, ACT Australia
| | - Christian J. Lueck
- Department of Neurology, Canberra Hospital, Canberra, ACT Australia
- ANU Medical School, College of Health and Medicine, Australian National University, Canberra, ACT Australia
| | - Hanna Suominen
- School of Computing, College of Engineering and Computer Science, Australian National University, Canberra, ACT Australia
- Department of Computing, University of Turku, Turku, Finland
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18
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Chen Q, Du J, Allot A, Lu Z. LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2584-2595. [PMID: 35536809 PMCID: PMC9647722 DOI: 10.1109/tcbb.2022.3173562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 05/20/2023]
Abstract
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 200,000 articles with millions of accesses. Approximately 10,000 new articles are added to LitCovid every month. A main curation task in LitCovid is topic annotation where an article is assigned with up to eight topics, e.g., Treatment and Diagnosis. The annotated topics have been widely used both in LitCovid (e.g., accounting for ∼18% of total uses) and downstream studies such as network generation. However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth. This study proposes LITMC-BERT, a transformer-based multi-label classification method in biomedical literature. It uses a shared transformer backbone for all the labels while also captures label-specific features and the correlations between label pairs. We compare LITMC-BERT with three baseline models on two datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the current best results, respectively, and only requires ∼18% of the inference time than the Binary BERT baseline. The related datasets and models are available via https://github.com/ncbi/ml-transformer.
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19
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Chen Q, Allot A, Leaman R, Islamaj R, Du J, Fang L, Wang K, Xu S, Zhang Y, Bagherzadeh P, Bergler S, Bhatnagar A, Bhavsar N, Chang YC, Lin SJ, Tang W, Zhang H, Tavchioski I, Pollak S, Tian S, Zhang J, Otmakhova Y, Yepes AJ, Dong H, Wu H, Dufour R, Labrak Y, Chatterjee N, Tandon K, Laleye FAA, Rakotoson L, Chersoni E, Gu J, Friedrich A, Pujari SC, Chizhikova M, Sivadasan N, Vg S, Lu Z. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database (Oxford) 2022; 2022:baac069. [PMID: 36043400 PMCID: PMC9428574 DOI: 10.1093/database/baac069] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/02/2022] [Accepted: 08/13/2022] [Indexed: 05/03/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.
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Affiliation(s)
- Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Alexis Allot
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Robert Leaman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Rezarta Islamaj
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
| | - Jingcheng Du
- School of Biomedical Informatics, UT Health, TX, Houston 77030, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shuo Xu
- College of Economics and Management, Beijing University of Technology, Beijing, QC, China
| | - Yuefu Zhang
- College of Economics and Management, Beijing University of Technology, Beijing, QC, China
| | | | | | | | | | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Sheng-Jie Lin
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wentai Tang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Hongtong Zhang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Ilija Tavchioski
- Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
| | | | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Yulia Otmakhova
- School of Computing and Information Systems, University of Melbourne, Melbourne, AU-VIC, Australia
| | | | - Hang Dong
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Honghan Wu
- Institute of Health Informatics, University College London, London, UK
| | | | | | - Niladri Chatterjee
- Department of Mathematics, Indian Institute of Technology Delhi, New Delhi, India
| | - Kushagri Tandon
- Department of Mathematics, Indian Institute of Technology Delhi, New Delhi, India
| | | | | | - Emmanuele Chersoni
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jinghang Gu
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | | | - Subhash Chandra Pujari
- Institute of Computer Science, Heidelberg University, Heidelberg, Germany
- Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Mariia Chizhikova
- SINAI Group, Department of Computer Science, Advanced Studies Center in ICT (CEATIC), Universidad de Jaén, Jaén, Spain
| | | | | | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, Bethesda 20892, USA
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20
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Erdengasileng A, Han Q, Zhao T, Tian S, Sui X, Li K, Wang W, Wang J, Hu T, Pan F, Zhang Y, Zhang J. Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification. Database (Oxford) 2022; 2022:baac066. [PMID: 35962559 PMCID: PMC9375052 DOI: 10.1093/database/baac066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 11/19/2022]
Abstract
Large volumes of publications are being produced in biomedical sciences nowadays with ever-increasing speed. To deal with the large amount of unstructured text data, effective natural language processing (NLP) methods need to be developed for various tasks such as document classification and information extraction. BioCreative Challenge was established to evaluate the effectiveness of information extraction methods in biomedical domain and facilitate their development as a community-wide effort. In this paper, we summarize our work and what we have learned from the latest round, BioCreative Challenge VII, where we participated in all five tracks. Overall, we found three key components for achieving high performance across a variety of NLP tasks: (1) pre-trained NLP models; (2) data augmentation strategies and (3) ensemble modelling. These three strategies need to be tailored towards the specific tasks at hands to achieve high-performing baseline models, which are usually good enough for practical applications. When further combined with task-specific methods, additional improvements (usually rather small) can be achieved, which might be critical for winning competitions. Database URL: https://doi.org/10.1093/database/baac066.
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Affiliation(s)
| | - Qing Han
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL 32306, USA
| | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Xin Sui
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Keqiao Li
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Wanjing Wang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Jian Wang
- Cloudmedx Inc, Palo Alto, CA 94301, USA
| | - Ting Hu
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Feng Pan
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Yuan Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
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21
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Abstract
In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.
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22
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Almeida T, Antunes R, F. Silva J, Almeida JR, Matos S. Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics. Database (Oxford) 2022; 2022:6625810. [PMID: 35776534 PMCID: PMC9248917 DOI: 10.1093/database/baac047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/13/2022] [Accepted: 06/06/2022] [Indexed: 11/14/2022]
Abstract
Abstract
The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available.
Database URL
https://github.com/bioinformatics-ua/biocreativeVII_track2
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Affiliation(s)
- Tiago Almeida
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Aveiro, Portugal
| | - Rui Antunes
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Aveiro, Portugal
| | - João F. Silva
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Aveiro, Portugal
| | - João R Almeida
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Aveiro, Portugal
- Department of Information and Communications Technologies, University of A Coruña , A Coruña, Spain
| | - Sérgio Matos
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Aveiro, Portugal
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23
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Rodriguez-Esteban R. New reasons for biologists to write with a formal language. Database (Oxford) 2022; 2022:6600538. [PMID: 35657112 PMCID: PMC9216469 DOI: 10.1093/database/baac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/18/2022] [Accepted: 05/17/2022] [Indexed: 12/03/2022]
Abstract
Current biological writing is afflicted by the use of ambiguous names, convoluted sentences, vague statements and narrative-fitted storylines. This represents a challenge for biological research in general and in particular for fields such as biological database curation and text mining, which have been tasked to cope with exponentially growing content. Improving the quality of biological writing by encouraging unambiguity and precision would foster expository discipline and machine reasoning. More specifically, the routine inclusion of formal languages in biological writing would improve our ability to describe, compile and model biology.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124 , Basel 4070, Switzerland
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24
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Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.10.100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Bhasuran B. Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries. Methods Mol Biol 2022; 2496:123-140. [PMID: 35713862 DOI: 10.1007/978-1-0716-2305-3_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The major outcomes and insights of scientific research and clinical study end up in the form of publication or clinical record in an unstructured text format. Due to advancements in biomedical research, the growth of published literature is getting tremendous large in recent years. The scientists and clinical researchers are facing a big challenge to stay current with the knowledge and to extract hidden information from this sheer quantity of millions of published biomedical literature. The potential one-stop automated solution to this problem is biomedical literature mining. One of the long-standing goals in biology is to discover the disease-causing genes and their specific roles in personalized precision medicine and drug repurposing. However, the empirical approaches and clinical affirmation are expensive and time-consuming. In silico approach using text mining to identify the disease causing genes can contribute towards biomarker discovery. This chapter presents a protocol on combining literature mining and machine learning for predicting biomedical discoveries with a special emphasis on gene-disease relation based discovery. The protocol is presented as a literature based discovery (LBD) pipeline for gene-disease based discovery. The protocol includes our web based tools: (1) DNER (Disease Named Entity Recognizer) for disease entity recognition, (2) BCCNER (Bidirectional, Contextual clues Named Entity Tagger) for gene/protein entity recognition, (3) DisGeReExT (Disease-Gene Relation Extractor) for statistically validated results and visualization, and (4) a newly introduced deep learning based method for association discovery. Our proposed deep learning based method can be generalized and applied to other important biomedical discoveries focusing on entities such as drug/chemical, or miRNA.
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Affiliation(s)
- Balu Bhasuran
- DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
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26
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StaResGRU-CNN with CMedLMs: A stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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27
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Converting Biomedical Text Annotated Resources into FAIR Research Objects with an Open Science Platform. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11209648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Today, there are excellent resources for the semantic annotation of biomedical text. These resources span from ontologies, tools for NLP, annotators, and web services. Most of these are available either in the form of open source components (i.e., MetaMap) or as web services that offer free access (i.e., Whatizit). In order to use these resources in automatic text annotation pipelines, researchers face significant technical challenges. For open-source tools, the challenges include the setting up of the computational environment, the resolution of dependencies, as well as the compilation and installation of the software. For web services, the challenge is implementing clients to undertake communication with the respective web APIs. Even resources that are available as Docker containers (i.e., NCBO annotator) require significant technical skills for installation and setup. This work deals with the task of creating ready-to-install and run Research Objects (ROs) for a large collection of components in biomedical text analysis. These components include (a) tools such as cTAKES, NOBLE Coder, MetaMap, NCBO annotator, BeCAS, and Neji; (b) ontologies from BioPortal, NCBI BioSystems, and Open Biomedical Ontologies; and (c) text corpora such as BC4GO, Mantra Gold Standard Corpus, and the COVID-19 Open Research Dataset. We make these resources available in OpenBio.eu, an open-science RO repository and workflow management system. All ROs can be searched, shared, edited, downloaded, commented on, and rated. We also demonstrate how one can easily connect these ROs to form a large variety of text annotation pipelines.
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28
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Parolo S, Tomasoni D, Bora P, Ramponi A, Kaddi C, Azer K, Domenici E, Neves-Zaph S, Lombardo R. Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis. Front Cell Dev Biol 2021; 9:703489. [PMID: 34490253 PMCID: PMC8417786 DOI: 10.3389/fcell.2021.703489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.
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Affiliation(s)
- Silvia Parolo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Pranami Bora
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alan Ramponi
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Enrico Domenici
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Rosario Lombardo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
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29
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Zeng K, Xu Y, Lin G, Liang L, Hao T. Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning. BMC Med Inform Decis Mak 2021; 21:129. [PMID: 34330259 PMCID: PMC8323220 DOI: 10.1186/s12911-021-01492-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 04/08/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. METHODS An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. RESULTS Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. CONCLUSIONS A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.
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Affiliation(s)
- Kun Zeng
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Yibin Xu
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Ge Lin
- National Engineering Research Center of Digital Life, Sun Yat-Sen University, Guangzhou, China
| | - Likeng Liang
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Tianyong Hao
- School of Computer Science, South China Normal University, Guangzhou, China
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30
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Queirós P, Novikova P, Wilmes P, May P. Unification of functional annotation descriptions using text mining. Biol Chem 2021; 402:983-990. [PMID: 33984880 DOI: 10.1515/hsz-2021-0125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/03/2021] [Indexed: 02/06/2023]
Abstract
A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it is possible to identify highly similar functional descriptions, thus strengthening the confidence of the final protein functional annotation and providing a redundancy-free output. Here we present UniFunc, a text mining approach that is able to detect similar functional descriptions with high precision. UniFunc was built as a small module and can be independently used or integrated into protein function annotation pipelines. By removing the need to individually analyse and compare annotation results, UniFunc streamlines the complementary use of multiple reference datasets.
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Affiliation(s)
| | | | - Paul Wilmes
- Systems Ecology, Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4362, Esch-sur-Alzette, Luxembourg
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31
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Miftahutdinov Z, Kadurin A, Kudrin R, Tutubalina E. Medical Concept Normalization in Clinical Trials with Drug and Disease Representation Learning. Bioinformatics 2021; 37:3856-3864. [PMID: 34213526 PMCID: PMC8570806 DOI: 10.1093/bioinformatics/btab474] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/02/2021] [Accepted: 07/01/2021] [Indexed: 11/18/2022] Open
Abstract
Motivation Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery and development such instances are very rare. Presently large-scale information on clinical trials is stored in clinical trial registers which are relatively structured, but the mappings to external databases of drugs and diseases are increasingly lacking. The precise production of such links would enable us to interrogate richer harmonized datasets for invaluable insights. Results We present a neural approach for medical concept normalization of diseases and drugs. Our two-stage approach is based on Bidirectional Encoder Representations from Transformers (BERT). In the training stage, we optimize the relative similarity of mentions and concept names from a terminology via triplet loss. In the inference stage, we obtain the closest concept name representation in a common embedding space to a given mention representation. We performed a set of experiments on a dataset of abstracts and a real-world dataset of trial records with interventions and conditions mapped to drug and disease terminologies. The latter includes mentions associated with one or more concepts (in-KB) or zero (out-of-KB, nil prediction). Experiments show that our approach significantly outperforms baseline and state-of-the-art architectures. Moreover, we demonstrate that our approach is effective in knowledge transfer from the scientific literature to clinical trial data. Availability and implementation We make code and data freely available at https://github.com/insilicomedicine/DILBERT.
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Affiliation(s)
| | - Artur Kadurin
- Insilico Medicine Hong Kong, Pak Shek Kok, Hong Kong
| | - Roman Kudrin
- Insilico Medicine Hong Kong, Pak Shek Kok, Hong Kong
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32
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Queirós P, Delogu F, Hickl O, May P, Wilmes P. Mantis: flexible and consensus-driven genome annotation. Gigascience 2021; 10:giab042. [PMID: 34076241 PMCID: PMC8170692 DOI: 10.1093/gigascience/giab042] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/22/2021] [Accepted: 05/14/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources. RESULTS We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations. CONCLUSIONS Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.
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Affiliation(s)
- Pedro Queirós
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Francesco Delogu
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Oskar Hickl
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Paul Wilmes
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
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33
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Islamaj R, Wei CH, Cissel D, Miliaras N, Printseva O, Rodionov O, Sekiya K, Ward J, Lu Z. NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition. J Biomed Inform 2021; 118:103779. [PMID: 33839304 PMCID: PMC11037554 DOI: 10.1016/j.jbi.2021.103779] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 03/14/2021] [Accepted: 04/05/2021] [Indexed: 10/21/2022]
Abstract
The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. While current methods for tagging gene entities have been developed for biomedical literature, their performance on species other than human is substantially lower due to the lack of annotation data. We therefore present the NLM-Gene corpus, a high-quality manually annotated corpus for genes developed at the US National Library of Medicine (NLM), covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per document, and a broader representation of different species (including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed abstracts from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each document to control for bias. The annotators worked in three annotation rounds until they reached complete agreement. This gold-standard corpus can serve as a benchmark to develop & test new gene text mining algorithms. Using this new resource, we have developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene. We have also applied this tool to the entire PubMed/PMC with their results freely accessible through our web-based tool PubTator (www.ncbi.nlm.nih.gov/research/pubtator).
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Affiliation(s)
- Rezarta Islamaj
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Chih-Hsuan Wei
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David Cissel
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Nicholas Miliaras
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Olga Printseva
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Oleg Rodionov
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Keiko Sekiya
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Janice Ward
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhiyong Lu
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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34
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Zhao S, Su C, Lu Z, Wang F. Recent advances in biomedical literature mining. Brief Bioinform 2021; 22:bbaa057. [PMID: 32422651 PMCID: PMC8138828 DOI: 10.1093/bib/bbaa057] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/22/2020] [Accepted: 03/25/2020] [Indexed: 01/26/2023] Open
Abstract
The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.
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Affiliation(s)
- Sendong Zhao
- Department of Healthcare Policy and Research, Weill Medical College of Cornell University, New York, NY 10065, USA
| | - Chang Su
- Division of Health Informatics, Department of Healthcare Policy and Research at Weill Cornell Medicine at Cornell University, New York, NY, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI) at National Library of Medicine, National Institute of Health, Bethesda, MD, USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Medical College of Cornell University, New York, NY 10065, USA
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35
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Singh G, Papoutsoglou EA, Keijts-Lalleman F, Vencheva B, Rice M, Visser RG, Bachem CW, Finkers R. Extracting knowledge networks from plant scientific literature: potato tuber flesh color as an exemplary trait. BMC PLANT BIOLOGY 2021; 21:198. [PMID: 33894758 PMCID: PMC8070292 DOI: 10.1186/s12870-021-02943-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Scientific literature carries a wealth of information crucial for research, but only a fraction of it is present as structured information in databases and therefore can be analyzed using traditional data analysis tools. Natural language processing (NLP) is often and successfully employed to support humans by distilling relevant information from large corpora of free text and structuring it in a way that lends itself to further computational analyses. For this pilot, we developed a pipeline that uses NLP on biological literature to produce knowledge networks. We focused on the flesh color of potato, a well-studied trait with known associations, and we investigated whether these knowledge networks can assist us in formulating new hypotheses on the underlying biological processes. RESULTS We trained an NLP model based on a manually annotated corpus of 34 full-text potato articles, to recognize relevant biological entities and relationships between them in text (genes, proteins, metabolites and traits). This model detected the number of biological entities with a precision of 97.65% and a recall of 88.91% on the training set. We conducted a time series analysis on 4023 PubMed abstract of plant genetics-based articles which focus on 4 major Solanaceous crops (tomato, potato, eggplant and capsicum), to determine that the networks contained both previously known and contemporaneously unknown leads to subsequently discovered biological phenomena relating to flesh color. A novel time-based analysis of these networks indicates a connection between our trait and a candidate gene (zeaxanthin epoxidase) already two years prior to explicit statements of that connection in the literature. CONCLUSIONS Our time-based analysis indicates that network-assisted hypothesis generation shows promise for knowledge discovery, data integration and hypothesis generation in scientific research.
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Affiliation(s)
- Gurnoor Singh
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | | | | | | | - Mark Rice
- IBM Netherlands, Amsterdam, The Netherlands
| | - Richard G.F. Visser
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | - Christian W.B. Bachem
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | - Richard Finkers
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
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Turina P, Fariselli P, Capriotti E. ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed. Front Mol Biosci 2021; 8:620475. [PMID: 33842537 PMCID: PMC8027235 DOI: 10.3389/fmolb.2021.620475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 02/18/2021] [Indexed: 11/13/2022] Open
Abstract
During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts. Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.
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Affiliation(s)
- Paola Turina
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
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Wang LL, Lo K. Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Brief Bioinform 2021; 22:781-799. [PMID: 33279995 PMCID: PMC7799291 DOI: 10.1093/bib/bbaa296] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/02/2020] [Accepted: 10/07/2020] [Indexed: 12/13/2022] Open
Abstract
More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.
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Affiliation(s)
- Lucy Lu Wang
- The Allen Institute for Artificial Intelligence, Seattle, WA 98112, USA
| | - Kyle Lo
- The Allen Institute for Artificial Intelligence, Seattle, WA 98112, USA
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Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021; 12:13. [PMID: 34012717 PMCID: PMC8112352 DOI: 10.4103/jpi.jpi_84_20] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/28/2020] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.
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Affiliation(s)
| | | | | | | | | | | | - Norman Zerbe
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Berlin, Germany
- HTW University of Applied Sciences Berlin, Berlin, Germany
| | - Peter Hufnagl
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Berlin, Germany
- HTW University of Applied Sciences Berlin, Berlin, Germany
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Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. J Am Med Inform Assoc 2021; 27:13-21. [PMID: 31135882 DOI: 10.1093/jamia/ocz063] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/23/2019] [Accepted: 04/17/2019] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task. MATERIALS AND METHODS The clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches. RESULTS Our best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction. CONCLUSION In this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.
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Affiliation(s)
- Qiang Wei
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zongcheng Ji
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zhiheng Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Jingcheng Du
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jingqi Wang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jun Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yang Xiang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Firat Tiryaki
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Stephen Wu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yaoyun Zhang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Cui Tao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Incorporating bidirectional interactive information and regional features for relational facts extraction. AI OPEN 2021. [DOI: 10.1016/j.aiopen.2021.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Harris DR, Eisinger C, Wang Y, Delcher C. Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. PROCEEDINGS : ... IEEE INTERNATIONAL CONFERENCE ON BIG DATA. IEEE INTERNATIONAL CONFERENCE ON BIG DATA 2020; 2020:3727-3736. [PMID: 35282306 PMCID: PMC8910776 DOI: 10.1109/bigdata50022.2020.9378443] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We detail the challenges and barriers in applying natural language processing techniques to a collection of medical examiner case investigation notes related to fatal opioid poisonings. Major advances in biomedical informatics have made natural language processing (NLP) of medical texts both a realistic and useful task. Biomedical NLP tools are typically designed to process documents originating from biomedical libraries or electronic health records (EHRs). The usefulness of biomedical NLP tools on texts authored outside of EHRs is unclear, despite an abundance of medicolegal documents existing at the intersection of medicine and law. In particular, we detail our experiences processing unstructured text and extracting semantic concepts using case investigation notes; these notes were authored by trained investigative professionals working in a medical examiner's office and describe cases containing deaths related to fatal opioid poisonings. Applying NLP to case notes is a particularly important step in generalizing the advances of biomedical NLP for other related domains and giving guidance to data scientists working with unstructured data generated outside of EHRs.
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Affiliation(s)
- Daniel R Harris
- Institute for Pharmaceutical Outcomes and Policy, University of Kentucky, Lexington, Kentucky 40506
- Center for Clinical and Translational Sciences, University of Kentucky, Lexington, Kentucky 40506
| | - Christian Eisinger
- Institute for Pharmaceutical Outcomes and Policy, University of Kentucky, Lexington, Kentucky 40506
| | - Yanning Wang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida 32611
| | - Chris Delcher
- Institute for Pharmaceutical Outcomes and Policy, University of Kentucky, Lexington, Kentucky 40506
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Qu J, Steppi A, Zhong D, Hao J, Wang J, Lung PY, Zhao T, He Z, Zhang J. Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach. BMC Genomics 2020; 21:773. [PMID: 33167858 PMCID: PMC7654050 DOI: 10.1186/s12864-020-07185-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 10/26/2020] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. RESULTS Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. CONCLUSIONS The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.
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Affiliation(s)
- Jinchan Qu
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Albert Steppi
- Laboratory of Systems Pharmacology at Harvard Medical School, Boston, MA, 02115, USA
| | - Dongrui Zhong
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Jie Hao
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Jian Wang
- CloudMedx, Palo Alto, CA, 94301, USA
| | - Pei-Yau Lung
- Verisk - Insurance Solutions, Middletown, CT, 06457, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL, 32306, USA
| | - Zhe He
- College of Communication and Information, Florida State University, Tallahassee, FL, 32306, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
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Wang J, Li M, Diao Q, Lin H, Yang Z, Zhang Y. Biomedical document triage using a hierarchical attention-based capsule network. BMC Bioinformatics 2020; 21:380. [PMID: 32938366 PMCID: PMC7495737 DOI: 10.1186/s12859-020-03673-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences. RESULTS In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora. CONCLUSIONS Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.
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Affiliation(s)
- Jian Wang
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
| | - Mengying Li
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
| | - Qishuai Diao
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
| | - Hongfei Lin
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
| | - Zhihao Yang
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
| | - YiJia Zhang
- Dalian University of Technology, The School of Computer Science and Technology, Dalian, 116024 China
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Luong NDM, Coroller L, Zagorec M, Membré JM, Guillou S. Spoilage of Chilled Fresh Meat Products during Storage: A Quantitative Analysis of Literature Data. Microorganisms 2020; 8:E1198. [PMID: 32781668 PMCID: PMC7465036 DOI: 10.3390/microorganisms8081198] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 08/03/2020] [Indexed: 01/21/2023] Open
Abstract
A literature search was performed on spoilage of fresh meat products by combining keyword query, text mining and expert elicitation. From the 258 collected studies, a quantitative analysis was first performed to identify the methods which are the most used to evaluate spoilage beside the preservation strategies suggested. In a second step focusing on a subset of 24 publications providing quantitative data on spoilage occurrence time, associations between spoilage occurrence time of meat products and specific spoilage indicators were investigated. The analysis especially focused on factors well represented in the 24 publications, i.e., gas packaging (O2 and CO2) and storage temperature. Relationships between spoilage occurrence and several microbiological indicators were also sought. The results point out possible advantages of removing dioxygen in packaging to delay spoilage occurrence, whereas, in the presence of dioxygen, the carbon dioxide proportion in the gas mixtures was shown to influence spoilage occurrence. The collected data clearly reveal a potentially protective role of lactic acid bacteria. Besides, while a spoilage role could be attributed to Pseudomonas spp., the growth of mesophilic aerobic microbes, Brochothrix spp. and Enterobacteriaceae seemed independent of spoilage occurrence time.
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Affiliation(s)
- Ngoc-Du Martin Luong
- SECALIM, INRAE, ONIRIS, Université Bretagne Loire, Route de Gachet, CS 40706, F-44307 Nantes, France; (N.-D.M.L.); (M.Z.); (J.-M.M.)
| | - Louis Coroller
- Université de Brest, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, UMT Alter’ix, F-29334 Quimper, France;
| | - Monique Zagorec
- SECALIM, INRAE, ONIRIS, Université Bretagne Loire, Route de Gachet, CS 40706, F-44307 Nantes, France; (N.-D.M.L.); (M.Z.); (J.-M.M.)
| | - Jeanne-Marie Membré
- SECALIM, INRAE, ONIRIS, Université Bretagne Loire, Route de Gachet, CS 40706, F-44307 Nantes, France; (N.-D.M.L.); (M.Z.); (J.-M.M.)
| | - Sandrine Guillou
- SECALIM, INRAE, ONIRIS, Université Bretagne Loire, Route de Gachet, CS 40706, F-44307 Nantes, France; (N.-D.M.L.); (M.Z.); (J.-M.M.)
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Zeng K, Pan Z, Xu Y, Qu Y. An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation. JMIR Med Inform 2020; 8:e17832. [PMID: 32609092 PMCID: PMC7367522 DOI: 10.2196/17832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/09/2020] [Accepted: 03/14/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. OBJECTIVE We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. METHODS We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. RESULTS Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. CONCLUSIONS We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.
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Affiliation(s)
- Kun Zeng
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Zhiwei Pan
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yibin Xu
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Yingying Qu
- School of Business, Guangdong University of Foreign Studies, Guangzhou, China
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Leaman R, Wei CH, Allot A, Lu Z. Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability. PLoS Biol 2020; 18:e3000716. [PMID: 32479517 PMCID: PMC7289435 DOI: 10.1371/journal.pbio.3000716] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/11/2020] [Indexed: 12/22/2022] Open
Abstract
Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips—and a web tool, PubReCheck—guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors’ work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck. Your published research is already being processed with automated tools, and text mining will become more common; this Community Page article describes how you can help these tools process your work more accurately, including a web tool, PubReCheck.
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Affiliation(s)
- Robert Leaman
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Alexis Allot
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
- * E-mail:
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Abstract
Background Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types. Results We developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus. Conclusions With this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.
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Liu C, Peres Kury FS, Li Z, Ta C, Wang K, Weng C. Doc2Hpo: a web application for efficient and accurate HPO concept curation. Nucleic Acids Res 2020; 47:W566-W570. [PMID: 31106327 PMCID: PMC6602487 DOI: 10.1093/nar/gkz386] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/26/2019] [Accepted: 04/30/2019] [Indexed: 01/18/2023] Open
Abstract
We present Doc2Hpo, an interactive web application that enables interactive and efficient phenotype concept curation from clinical text with automated concept normalization using the Human Phenotype Ontology (HPO). Users can edit the HPO concepts automatically extracted by Doc2Hpo in real time, and export the extracted HPO concepts into gene prioritization tools. Our evaluation showed that Doc2Hpo significantly reduced manual effort while achieving high accuracy in HPO concept curation. Doc2Hpo is freely available at https://impact2.dbmi.columbia.edu/doc2hpo/. The source code is available at https://github.com/stormliucong/doc2hpo for local installation for protected health data.
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Affiliation(s)
- Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | | | - Ziran Li
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Kai Wang
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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50
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Wang X, Zhang Y, Ren X, Zhang Y, Zitnik M, Shang J, Langlotz C, Han J. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 2020; 35:1745-1752. [PMID: 30307536 DOI: 10.1093/bioinformatics/bty869] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 10/03/2018] [Accepted: 10/09/2018] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. RESULTS We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora. AVAILABILITY AND IMPLEMENTATION Our source code is available at https://github.com/yuzhimanhua/lm-lstm-crf. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xuan Wang
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yu Zhang
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Xiang Ren
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Yuhao Zhang
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Marinka Zitnik
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jingbo Shang
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Curtis Langlotz
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Jiawei Han
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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