1
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Yamagiwa H, Hashimoto R, Arakane K, Murakami K, Soeda S, Oyama M, Zhu Y, Okada M, Shimodaira H. Predicting drug-gene relations via analogy tasks with word embeddings. Sci Rep 2025; 15:17240. [PMID: 40383732 PMCID: PMC12086191 DOI: 10.1038/s41598-025-01418-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Accepted: 05/06/2025] [Indexed: 05/20/2025] Open
Abstract
Natural language processing is utilized in a wide range of fields, where words in text are typically transformed into feature vectors called embeddings. BioConceptVec is a specific example of embeddings tailored for biology, trained on approximately 30 million PubMed abstracts using models such as skip-gram. Generally, word embeddings are known to solve analogy tasks through simple vector arithmetic. For example, subtracting the vector for man from that of king and then adding the vector for woman yields a point that lies closer to queen in the embedding space. In this study, we demonstrate that BioConceptVec embeddings, along with our own embeddings trained on PubMed abstracts, contain information about drug-gene relations and can predict target genes from a given drug through analogy computations. We also show that categorizing drugs and genes using biological pathways improves performance. Furthermore, we illustrate that vectors derived from known relations in the past can predict unknown future relations in datasets divided by year. Despite the simplicity of implementing analogy tasks as vector additions, our approach demonstrated performance comparable to that of large language models such as GPT-4 in predicting drug-gene relations.
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Affiliation(s)
| | | | - Kiwamu Arakane
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Ken Murakami
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Shou Soeda
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Momose Oyama
- Kyoto University, Kyoto, Japan
- RIKEN, Tokyo, Japan
| | | | - Mariko Okada
- Institute for Protein Research, Osaka University, Osaka, Japan
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2
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Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer MB, Ai X, Lai PT, Wang Z, Keloth VK, Raja K, Huang J, He H, Lin F, Du J, Zhang R, Zheng WJ, Adelman RA, Lu Z, Xu H. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nat Commun 2025; 16:3280. [PMID: 40188094 PMCID: PMC11972378 DOI: 10.1038/s41467-025-56989-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/07/2025] [Indexed: 04/07/2025] Open
Abstract
The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown promise in general domains, their effectiveness in BioNLP tasks remains unclear due to limited benchmarks and practical guidelines. We perform a systematic evaluation of four LLMs-GPT and LLaMA representatives-on 12 BioNLP benchmarks across six applications. We compare their zero-shot, few-shot, and fine-tuning performance with the traditional fine-tuning of BERT or BART models. We examine inconsistencies, missing information, hallucinations, and perform cost analysis. Here, we show that traditional fine-tuning outperforms zero- or few-shot LLMs in most tasks. However, closed-source LLMs like GPT-4 excel in reasoning-related tasks such as medical question answering. Open-source LLMs still require fine-tuning to close performance gaps. We find issues like missing information and hallucinations in LLM outputs. These results offer practical insights for applying LLMs in BioNLP.
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Affiliation(s)
- Qingyu Chen
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Yan Hu
- McWilliams School of Biomedical Informatics, University of Texas Health Science at Houston, Houston, TX, USA
| | - Xueqing Peng
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Qianqian Xie
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Qiao Jin
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Aidan Gilson
- Department of Ophthalmology and Visual Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Maxwell B Singer
- Department of Ophthalmology and Visual Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Xuguang Ai
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Po-Ting Lai
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhizheng Wang
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Vipina K Keloth
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kalpana Raja
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Jimin Huang
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Huan He
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Fongci Lin
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Jingcheng Du
- McWilliams School of Biomedical Informatics, University of Texas Health Science at Houston, Houston, TX, USA
| | - Rui Zhang
- Division of Computational Health Sciences, Department of Surgery, Medical School, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - W Jim Zheng
- McWilliams School of Biomedical Informatics, University of Texas Health Science at Houston, Houston, TX, USA
| | - Ron A Adelman
- Department of Ophthalmology and Visual Science, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Zhiyong Lu
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
| | - Hua Xu
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
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3
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Chen Y, Zou J. Simple and effective embedding model for single-cell biology built from ChatGPT. Nat Biomed Eng 2025; 9:483-493. [PMID: 39643729 DOI: 10.1038/s41551-024-01284-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 10/16/2024] [Indexed: 12/09/2024]
Abstract
Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatGPT embeddings of genes based on the literature. We used GPT-3.5 to generate gene embeddings from text descriptions of individual genes and to then generate single-cell embeddings by averaging the gene embeddings weighted by each gene's expression level. We also created a sentence embedding for each cell by using only the gene names ordered by their expression level. On many downstream tasks used to evaluate pretrained single-cell embedding models-particularly, tasks of gene-property and cell-type classifications-our model, which we named GenePT, achieved comparable or better performance than models pretrained from gene-expression profiles of millions of cells. GenePT shows that large-language-model embeddings of the literature provide a simple and effective path to encoding single-cell biological knowledge.
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Affiliation(s)
- Yiqun Chen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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4
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Brown GS, Wengler J, Fabelico AJS, Muir A, Tubbs A, Warren A, Millett AN, Yu XX, Pavlidis P, Rogic S, Piccolo SR. Using semantic search to find publicly available gene-expression datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.13.643153. [PMID: 40161731 PMCID: PMC11952526 DOI: 10.1101/2025.03.13.643153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Millions of high-throughput, molecular datasets have been shared in public repositories. have been shared in public repositories. Researchers can reuse such data to validate their own findings and explore novel questions. A frequent goal is to find multiple datasets that address similar research topics and to either combine them directly or integrate inferences from them. However, a major challenge is finding relevant datasets due to the vast number of candidates, inconsistencies in their descriptions, and a lack of semantic annotations. This challenge is first among the FAIR principles for scientific data. Here we focus on dataset discovery within Gene Expression Omnibus (GEO), a repository containing 100,000s of data series. GEO supports queries based on keywords, ontology terms, and other annotations. However, reviewing these results is time-consuming and tedious, and it often misses relevant datasets. We hypothesized that language models could address this problem by summarizing dataset descriptions as numeric representations (embeddings). Assuming a researcher has previously found some relevant datasets, we evaluated the potential to find additional relevant datasets. For six human medical conditions, we used 30 models to generate embeddings for datasets that human curators had previously associated with the conditions and identified other datasets with the most similar descriptions. This approach was often, but not always, more effective than GEO's search engine. Our top-performing models were trained on general corpora, used contrastive-learning strategies, and used relatively large embeddings. Our findings suggest that language models have the potential to improve dataset discovery, perhaps in combination with existing search tools.
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Affiliation(s)
- Grace S. Brown
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - James Wengler
- Department of Biology, Brigham Young University, Provo, Utah, USA
- Institute of Biosciences and Technology, Texas A&M Health Science Center, Houston, TX, USA
| | | | - Abigail Muir
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Anna Tubbs
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Amanda Warren
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Alexandra N. Millett
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Xinrui Xiang Yu
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Paul Pavlidis
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sanja Rogic
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
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Huang DL, Zeng Q, Xiong Y, Liu S, Pang C, Xia M, Fang T, Ma Y, Qiang C, Zhang Y, Zhang Y, Li H, Yuan Y. A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature. Interdiscip Sci 2024; 16:333-344. [PMID: 38340264 PMCID: PMC11289304 DOI: 10.1007/s12539-024-00605-2] [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: 12/12/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/12/2024]
Abstract
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types-gene, variant, disease and species. Both a BERT-based large name entity recognition (NER) model and a DistilBERT-based simplified NER model were trained, validated and tested, respectively. Due to the limited manually annotated corpus, Such NER models were fine-tuned with two phases. The F1-scores of BERT-based NER for gene, variant, disease and species are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those of DistilBERT-based NER are 95.14%, 86.26%, 91.37% and 89.92%, respectively. Most importantly, the entity type of variant has been extracted by a large language model for the first time and a comparable F1-score with the state-of-the-art variant extraction model tmVar has been achieved.
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Affiliation(s)
- Dao-Ling Huang
- BGI Research, Shenzhen, 518083, China.
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Quanlei Zeng
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yun Xiong
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Shuixia Liu
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Chaoqun Pang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Menglei Xia
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Ting Fang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yanli Ma
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Cuicui Qiang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yi Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yu Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Hong Li
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yuying Yuan
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China
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6
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Kang H, Hou L, Gu Y, Lu X, Li J, Li Q. Drug-disease association prediction with literature based multi-feature fusion. Front Pharmacol 2023; 14:1205144. [PMID: 37284317 PMCID: PMC10239876 DOI: 10.3389/fphar.2023.1205144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.
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Affiliation(s)
- Hongyu Kang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaowen Gu
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiao Lu
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qin Li
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
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7
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Liu Y, Elsworth BL, Gaunt TR. Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets. Bioinformatics 2023; 39:btad169. [PMID: 37010521 PMCID: PMC10097433 DOI: 10.1093/bioinformatics/btad169] [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: 09/23/2021] [Revised: 02/12/2023] [Accepted: 03/19/2023] [Indexed: 04/04/2023] Open
Abstract
MOTIVATION Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposome. Mapping trait names across large datasets is therefore time-consuming and challenging. Recent developments in language modelling have created new methods for semantic representation of words and phrases, and these methods offer new opportunities to map human trait names in the form of words and short phrases, both to ontologies and to each other. Here, we present a comparison between a range of established and more recent language modelling approaches for the task of mapping trait names from UK Biobank to the Experimental Factor Ontology (EFO), and also explore how they compare to each other in direct trait-to-trait mapping. RESULTS In our analyses of 1191 traits from UK Biobank with manual EFO mappings, the BioSentVec model performed best at predicting these, matching 40.3% of the manual mappings correctly. The BlueBERT-EFO model (finetuned on EFO) performed nearly as well (38.8% of traits matching the manual mapping). In contrast, Levenshtein edit distance only mapped 22% of traits correctly. Pairwise mapping of traits to each other demonstrated that many of the models can accurately group similar traits based on their semantic similarity. AVAILABILITY AND IMPLEMENTATION Our code is available at https://github.com/MRCIEU/vectology.
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Affiliation(s)
- Yi Liu
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | | | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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8
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Hussain MJ, Bai H, Wasti SH, Huang G, Jiang Y. Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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9
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Shtar G, Greenstein-Messica A, Mazuz E, Rokach L, Shapira B. Predicting drug characteristics using biomedical text embedding. BMC Bioinformatics 2022; 23:526. [PMID: 36476573 PMCID: PMC9730627 DOI: 10.1186/s12859-022-05083-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug-drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug's existing interactions, such an approach is unsuitable, and other drug's preferences can be used to accurately predict new Drug-drug interactions. METHODS We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs' interactions and the drug's biomedical text embeddings to predict the DDIs of both new and well known drugs. RESULTS Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs' biomedical prediction task by presenting text embedding's contribution to a multi-modal pregnancy drug safety classification. CONCLUSION Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug-drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.
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Affiliation(s)
- Guy Shtar
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Asnat Greenstein-Messica
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Eyal Mazuz
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lior Rokach
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Bracha Shapira
- grid.7489.20000 0004 1937 0511Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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10
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Lara-Clares A, Lastra-Díaz JJ, Garcia-Serrano A. A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art. PLoS One 2022; 17:e0276539. [PMID: 36409715 PMCID: PMC9678326 DOI: 10.1371/journal.pone.0276539] [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: 02/21/2022] [Accepted: 10/08/2022] [Indexed: 11/22/2022] Open
Abstract
This registered report introduces the largest, and for the first time, reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate for the first time an unexplored benchmark, called Corpus-Transcriptional-Regulation (CTR); (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (6) to bridge the lack of software and data reproducibility resources for methods and experiments in this line of research. Our reproducible experimental survey is based on a single software platform, which is provided with a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results. In addition, we introduce a new aggregated string-based sentence similarity method, called LiBlock, together with eight variants of current ontology-based methods, and a new pre-trained word embedding model trained on the full-text articles in the PMC-BioC corpus. Our experiments show that our novel string-based measure establishes the new state of the art in sentence similarity analysis in the biomedical domain and significantly outperforms all the methods evaluated herein, with the only exception of one ontology-based method. Likewise, our experiments confirm that the pre-processing stages, and the choice of the NER tool for ontology-based methods, have a very significant impact on the performance of the sentence similarity methods. We also detail some drawbacks and limitations of current methods, and highlight the need to refine the current benchmarks. Finally, a notable finding is that our new string-based method significantly outperforms all state-of-the-art Machine Learning (ML) models evaluated herein.
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Affiliation(s)
- Alicia Lara-Clares
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Juan J. Lastra-Díaz
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Ana Garcia-Serrano
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
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Turki H, Jemielniak D, Hadj Taieb MA, Labra Gayo JE, Ben Aouicha M, Banat M, Shafee T, Prud’hommeaux E, Lubiana T, Das D, Mietchen D. Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata. PeerJ Comput Sci 2022; 8:e1085. [PMID: 36262159 PMCID: PMC9575845 DOI: 10.7717/peerj-cs.1085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
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Affiliation(s)
- Houcemeddine Turki
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Dariusz Jemielniak
- Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Masovia, Poland
| | - Mohamed A. Hadj Taieb
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Jose E. Labra Gayo
- Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Oviedo, Asturias, Spain
| | - Mohamed Ben Aouicha
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Mus’ab Banat
- Faculty of Medicine, Hashemite University, Zarqa, Jordan
| | - Thomas Shafee
- La Trobe University, Melbourne, Victoria, Australia
- Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Eric Prud’hommeaux
- World Wide Web Consortium, Cambridge, Massachusetts, United States of America
| | - Tiago Lubiana
- Computational Systems Biology Laboratory, University of São Paulo, São Paulo, Brazil
| | - Diptanshu Das
- Institute of Child Health (ICH), Kolkata, West Bengal, India
- Medica Superspecialty Hospital, Kolkata, West Bengal, India
| | - Daniel Mietchen
- Ronin Institute, Montclair, New Jersey, United States of America
- Department of Evolutionary and Integrative Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
- School of Data Science, University of Virginia, Charlottesville, Virginia, United States
- Institute for Globally Distributed Open Research and Education (IGDORE), Jena, Germany
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12
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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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13
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Chen Q, Rankine A, Peng Y, Aghaarabi E, Lu Z. Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study. JMIR Med Inform 2021; 9:e27386. [PMID: 34967748 PMCID: PMC8759018 DOI: 10.2196/27386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 01/23/2023] Open
Abstract
Background Semantic textual similarity (STS) measures the degree of relatedness between sentence pairs. The Open Health Natural Language Processing (OHNLP) Consortium released an expertly annotated STS data set and called for the National Natural Language Processing Clinical Challenges. This work describes our entry, an ensemble model that leverages a range of deep learning (DL) models. Our team from the National Library of Medicine obtained a Pearson correlation of 0.8967 in an official test set during 2019 National Natural Language Processing Clinical Challenges/Open Health Natural Language Processing shared task and achieved a second rank. Objective Although our models strongly correlate with manual annotations, annotator-level correlation was only moderate (weighted Cohen κ=0.60). We are cautious of the potential use of DL models in production systems and argue that it is more critical to evaluate the models in-depth, especially those with extremely high correlations. In this study, we benchmark the effectiveness and efficiency of top-ranked DL models. We quantify their robustness and inference times to validate their usefulness in real-time applications. Methods We benchmarked five DL models, which are the top-ranked systems for STS tasks: Convolutional Neural Network, BioSentVec, BioBERT, BlueBERT, and ClinicalBERT. We evaluated a random forest model as an additional baseline. For each model, we repeated the experiment 10 times, using the official training and testing sets. We reported 95% CI of the Wilcoxon rank-sum test on the average Pearson correlation (official evaluation metric) and running time. We further evaluated Spearman correlation, R², and mean squared error as additional measures. Results Using only the official training set, all models obtained highly effective results. BioSentVec and BioBERT achieved the highest average Pearson correlations (0.8497 and 0.8481, respectively). BioSentVec also had the highest results in 3 of 4 effectiveness measures, followed by BioBERT. However, their robustness to sentence pairs of different similarity levels varies significantly. A particular observation is that BERT models made the most errors (a mean squared error of over 2.5) on highly similar sentence pairs. They cannot capture highly similar sentence pairs effectively when they have different negation terms or word orders. In addition, time efficiency is dramatically different from the effectiveness results. On average, the BERT models were approximately 20 times and 50 times slower than the Convolutional Neural Network and BioSentVec models, respectively. This results in challenges for real-time applications. Conclusions Despite the excitement of further improving Pearson correlations in this data set, our results highlight that evaluations of the effectiveness and efficiency of STS models are critical. In future, we suggest more evaluations on the generalization capability and user-level testing of the models. We call for community efforts to create more biomedical and clinical STS data sets from different perspectives to reflect the multifaceted notion of sentence-relatedness.
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Affiliation(s)
- Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Alex Rankine
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.,Harvard College, Cambridge, MA, United States
| | - Yifan Peng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.,Weill Cornell Medicine, New York, NY, United States
| | - Elaheh Aghaarabi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.,Towson University, Towson, MD, United States
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
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14
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Alachram H, Chereda H, Beißbarth T, Wingender E, Stegmaier P. Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks. PLoS One 2021; 16:e0258623. [PMID: 34653224 PMCID: PMC8519453 DOI: 10.1371/journal.pone.0258623] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 10/01/2021] [Indexed: 11/18/2022] Open
Abstract
Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on large breast cancer gene expression data and on other cancer datasets. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed sufficiently good for the metastatic event prediction tasks compared to other networks. Such performance was good enough to validate the utility of our generated word embeddings in constructing biological networks. Word representations as produced by text mining algorithms like word2vec, therefore are able to capture biologically meaningful relations between entities. Our generated embeddings are publicly available at https://github.com/genexplain/Word2vec-based-Networks/blob/main/README.md.
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Affiliation(s)
- Halima Alachram
- Department of Medical Bioinformatics, University Medical Center, Göttingen, Lower Saxony, Germany
| | - Hryhorii Chereda
- Department of Medical Bioinformatics, University Medical Center, Göttingen, Lower Saxony, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center, Göttingen, Lower Saxony, Germany
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15
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Holmgren SD, Boyles RR, Cronk RD, Duncan CG, Kwok RK, Lunn RM, Osborn KC, Thessen AE, Schmitt CP. Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8985. [PMID: 34501574 PMCID: PMC8430534 DOI: 10.3390/ijerph18178985] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/13/2021] [Accepted: 08/19/2021] [Indexed: 01/10/2023]
Abstract
Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.
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Affiliation(s)
- Stephanie D. Holmgren
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA;
| | | | | | - Christopher G. Duncan
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, NIEHS, Durham, NC 27709, USA;
| | - Richard K. Kwok
- Epidemiology Branch, Division of Intramural Research, NIEHS, Durham, NC 27709, USA;
- Office of the Director, NIEHS, Bethesda, MD 20892, USA
| | - Ruth M. Lunn
- Integrative Health Assessment Branch, Division of the National Toxicology Program, NIEHS, Durham, NC 27709, USA;
| | | | - Anne E. Thessen
- Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR 97331, USA;
| | - Charles P. Schmitt
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA;
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16
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Grissette H, Nfaoui EH. Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks. Cognit Comput 2021; 14:274-299. [PMID: 34422122 PMCID: PMC8371039 DOI: 10.1007/s12559-021-09903-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 06/23/2021] [Indexed: 11/30/2022]
Abstract
The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.
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Affiliation(s)
- Hanane Grissette
- LISAC Laboratory, Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - El Habib Nfaoui
- LISAC Laboratory, Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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17
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Grissette H, Nfaoui EH. Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients' narratives. PERSONAL AND UBIQUITOUS COMPUTING 2021; 27:1-15. [PMID: 34393692 PMCID: PMC8355270 DOI: 10.1007/s00779-021-01595-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
Owing to the quick spread of minute health-related experiences, the distillation of knowledge from such unstructured narratives is an extremely challenging task. In spite of the success of neural networks methods in improving learning structural reliability, they result in inadequate accuracy of the bio-medical sentiment classification when employing less useful features sets. Therefore, they lack discriminatory potential. In this study, we propose to add a deep associative memory into neural networks for an effective sentiment decomposition, which emphasizes correctly on bio-medical entities related to the extraction of different data-object properties, and contextual-semantics dependencies for a given aspect. The underlying trust of these measures is behind the ability to compute the completion of unseen medical patterns, where comprehensive bio-medical distributed representations are used for representing the formal medical connections from PubMed databases. Experiments on a biomedical sentiment analysis task show that the model provides comprehensive embeddings with meaningful medical patterns. It achieved an average performance of 87% on varied large online datasets. It also outperforms baselines in discovering and identifying medical natural concepts. We provide meaningful support to bio-medical sentiment analysis applications in social networks. Indeed, the facets of this study might be used in many health concerns such as analyzing change in health status or unexpected situations.
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Affiliation(s)
- Hanane Grissette
- LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - El Habib Nfaoui
- LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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18
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Song B, Li F, Liu Y, Zeng X. Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison. Brief Bioinform 2021; 22:6326536. [PMID: 34308472 DOI: 10.1093/bib/bbab282] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/07/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022] Open
Abstract
The biomedical literature is growing rapidly, and the extraction of meaningful information from the large amount of literature is increasingly important. Biomedical named entity (BioNE) identification is one of the critical and fundamental tasks in biomedical text mining. Accurate identification of entities in the literature facilitates the performance of other tasks. Given that an end-to-end neural network can automatically extract features, several deep learning-based methods have been proposed for BioNE recognition (BioNER), yielding state-of-the-art performance. In this review, we comprehensively summarize deep learning-based methods for BioNER and datasets used in training and testing. The deep learning methods are classified into four categories: single neural network-based, multitask learning-based, transfer learning-based and hybrid model-based methods. They can be applied to BioNER in multiple domains, and the results are determined by the dataset size and type. Lastly, we discuss the future development and opportunities of BioNER methods.
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Affiliation(s)
- Bosheng Song
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Fen Li
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Yuansheng Liu
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
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19
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Pfrieger FW. TeamTree analysis: A new approach to evaluate scientific production. PLoS One 2021; 16:e0253847. [PMID: 34288914 PMCID: PMC8294527 DOI: 10.1371/journal.pone.0253847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 06/14/2021] [Indexed: 11/18/2022] Open
Abstract
Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored. Notably, appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines.
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Affiliation(s)
- Frank W. Pfrieger
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, Strasbourg, France
- * E-mail: ,
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20
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Majewska O, Collins C, Baker S, Björne J, Brown SW, Korhonen A, Palmer M. BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. J Biomed Semantics 2021; 12:12. [PMID: 34266499 PMCID: PMC8280585 DOI: 10.1186/s13326-021-00247-z] [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: 01/28/2021] [Accepted: 07/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.
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Affiliation(s)
- Olga Majewska
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.
| | - Charlotte Collins
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Simon Baker
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Jari Björne
- Department of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland
| | - Susan Windisch Brown
- Department of Linguistics, University of Colorado Boulder, 295 UCB, Boulder, 80309-0295, Colorado, USA
| | - Anna Korhonen
- Language Technology Laboratory, MMLL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK
| | - Martha Palmer
- Department of Linguistics, University of Colorado Boulder, 295 UCB, Boulder, 80309-0295, Colorado, USA
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21
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Liu Z, Roberts RA, Lal-Nag M, Chen X, Huang R, Tong W. AI-based language models powering drug discovery and development. Drug Discov Today 2021; 26:2593-2607. [PMID: 34216835 PMCID: PMC8604259 DOI: 10.1016/j.drudis.2021.06.009] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 04/28/2021] [Accepted: 06/25/2021] [Indexed: 02/08/2023]
Abstract
The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.
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Affiliation(s)
- Zhichao Liu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Ruth A Roberts
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Madhu Lal-Nag
- Office of Translational Sciences, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD 20993, USA
| | - Xi Chen
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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22
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Yum Y, Lee JM, Jang MJ, Kim Y, Kim JH, Kim S, Shin U, Song S, Joo HJ. A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation. JMIR Med Inform 2021; 9:e29667. [PMID: 34185005 PMCID: PMC8277378 DOI: 10.2196/29667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/08/2021] [Accepted: 05/16/2021] [Indexed: 01/16/2023] Open
Abstract
Background The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. Objective We propose a new Korean word pair reference set to verify embedding models. Methods From January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs. Results The proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P<.001). The intraclass correlation coefficients to assess the interrater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (namu, ρ=0.12 vs with medical text for the similarity task, ρ=0.47; namu, ρ=0.02 vs with medical text for the relatedness task, ρ=0.30). Conclusions Korean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future.
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Affiliation(s)
- Yunjin Yum
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.,Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea
| | - Jeong Moon Lee
- Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea
| | - Moon Joung Jang
- Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea
| | - Yoojoong Kim
- Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea
| | - Jong-Ho Kim
- Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea.,Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seongtae Kim
- Department of Linguistics, Korea University, Seoul, Republic of Korea
| | - Unsub Shin
- Department of Linguistics, Korea University, Seoul, Republic of Korea
| | - Sanghoun Song
- Department of Linguistics, Korea University, Seoul, Republic of Korea
| | - Hyung Joon Joo
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul, Republic of Korea.,Korea University Research Institute for Medical Bigdata Science, Korea University Anam Hospital, Seoul, Republic of Korea.,Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
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23
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Newman-Griffis D, Sivaraman V, Perer A, Fosler-Lussier E, Hochheiser H. TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora. PROCEEDINGS OF THE CONFERENCE. ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. NORTH AMERICAN CHAPTER. MEETING 2021; 2021:106-115. [PMID: 34151319 PMCID: PMC8212692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.
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Affiliation(s)
| | | | - Adam Perer
- Human-Computer Interaction Institute, Carnegie Mellon University
| | | | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh
- Intelligent Systems Program, University of Pittsburgh
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Lara-Clares A, Lastra-Díaz JJ, Garcia-Serrano A. Protocol for a reproducible experimental survey on biomedical sentence similarity. PLoS One 2021; 16:e0248663. [PMID: 33760855 PMCID: PMC7990182 DOI: 10.1371/journal.pone.0248663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/02/2021] [Indexed: 11/28/2022] Open
Abstract
Measuring semantic similarity between sentences is a significant task in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and biomedical text mining. For this reason, the proposal of sentence similarity methods for the biomedical domain has attracted a lot of attention in recent years. However, most sentence similarity methods and experimental results reported in the biomedical domain cannot be reproduced for multiple reasons as follows: the copying of previous results without confirmation, the lack of source code and data to replicate both methods and experiments, and the lack of a detailed definition of the experimental setup, among others. As a consequence of this reproducibility gap, the state of the problem can be neither elucidated nor new lines of research be soundly set. On the other hand, there are other significant gaps in the literature on biomedical sentence similarity as follows: (1) the evaluation of several unexplored sentence similarity methods which deserve to be studied; (2) the evaluation of an unexplored benchmark on biomedical sentence similarity, called Corpus-Transcriptional-Regulation (CTR); (3) a study on the impact of the pre-processing stage and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (4) the lack of software and data resources for the reproducibility of methods and experiments in this line of research. Identified these open problems, this registered report introduces a detailed experimental setup, together with a categorization of the literature, to develop the largest, updated, and for the first time, reproducible experimental survey on biomedical sentence similarity. Our aforementioned experimental survey will be based on our own software replication and the evaluation of all methods being studied on the same software platform, which will be specially developed for this work, and it will become the first publicly available software library for biomedical sentence similarity. Finally, we will provide a very detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.
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Affiliation(s)
- Alicia Lara-Clares
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Juan J. Lastra-Díaz
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Ana Garcia-Serrano
- NLP & IR Research Group, E.T.S.I. Informática, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
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Chen Q, Allot A, Lu Z. LitCovid: an open database of COVID-19 literature. Nucleic Acids Res 2021; 49:D1534-D1540. [PMID: 33166392 PMCID: PMC7778958 DOI: 10.1093/nar/gkaa952] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/02/2020] [Accepted: 10/08/2020] [Indexed: 12/22/2022] Open
Abstract
Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.
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Affiliation(s)
- Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Alexis Allot
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA
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Abstract
A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.
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Affiliation(s)
- Marcin P. Joachimiak
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
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Yeganova L, Kim S, Chen Q, Balasanov G, Wilbur WJ, Lu Z. Better synonyms for enriching biomedical search. J Am Med Inform Assoc 2020; 27:1894-1902. [PMID: 33083825 PMCID: PMC7727334 DOI: 10.1093/jamia/ocaa151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/20/2020] [Accepted: 08/20/2020] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE In a biomedical literature search, the link between a query and a document is often not established, because they use different terms to refer to the same concept. Distributional word embeddings are frequently used for detecting related words by computing the cosine similarity between them. However, previous research has not established either the best embedding methods for detecting synonyms among related word pairs or how effective such methods may be. MATERIALS AND METHODS In this study, we first create the BioSearchSyn set, a manually annotated set of synonyms, to assess and compare 3 widely used word-embedding methods (word2vec, fastText, and GloVe) in their ability to detect synonyms among related pairs of words. We demonstrate the shortcomings of the cosine similarity score between word embeddings for this task: the same scores have very different meanings for the different methods. To address the problem, we propose utilizing pool adjacent violators (PAV), an isotonic regression algorithm, to transform a cosine similarity into a probability of 2 words being synonyms. RESULTS Experimental results using the BioSearchSyn set as a gold standard reveal which embedding methods have the best performance in identifying synonym pairs. The BioSearchSyn set also allows converting cosine similarity scores into probabilities, which provides a uniform interpretation of the synonymy score over different methods. CONCLUSIONS We introduced the BioSearchSyn corpus of 1000 term pairs, which allowed us to identify the best embedding method for detecting synonymy for biomedical search. Using the proposed method, we created PubTermVariants2.0: a large, automatically extracted set of synonym pairs that have augmented PubMed searches since the spring of 2019.
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Affiliation(s)
- Lana Yeganova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Sun Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Grigory Balasanov
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - W John Wilbur
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
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Makrodimitris S, van Ham RCHJ, Reinders MJT. Automatic Gene Function Prediction in the 2020's. Genes (Basel) 2020; 11:E1264. [PMID: 33120976 PMCID: PMC7692357 DOI: 10.3390/genes11111264] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023] Open
Abstract
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
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Affiliation(s)
- Stavros Makrodimitris
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Keygene N.V., 6708PW Wageningen, The Netherlands
| | - Roeland C. H. J. van Ham
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Keygene N.V., 6708PW Wageningen, The Netherlands
| | - Marcel J. T. Reinders
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands
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