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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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Marchesin S, Menotti L, Giachelle F, Silvello G, Alonso O. Building a large gene expression-cancer knowledge base with limited human annotations. Database (Oxford) 2023; 2023:baad061. [PMID: 37768281 PMCID: PMC10533344 DOI: 10.1093/database/baad061] [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: 05/26/2023] [Revised: 07/27/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.
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Affiliation(s)
- Stefano Marchesin
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Laura Menotti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Fabio Giachelle
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Omar Alonso
- Applied Science, Amazon, 3075 Olcott St., Santa Clara, California 95054, USA
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Marchesin S, Silvello G. TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction. BMC Bioinformatics 2022; 23:111. [PMID: 35361129 PMCID: PMC8973894 DOI: 10.1186/s12859-022-04646-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 03/22/2022] [Indexed: 01/12/2023] Open
Abstract
Background Databases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs) is one of BioRE most relevant tasks. Nevertheless, few resources have been developed to train models for GDA extraction. Besides, these resources are all limited in size—preventing models from scaling effectively to large amounts of data. Results To overcome this limitation, we have exploited the DisGeNET database to build a large-scale, semi-automatically annotated dataset for GDA extraction. DisGeNET stores one of the largest available collections of genes and variants involved in human diseases. Relying on DisGeNET, we developed TBGA: a GDA extraction dataset generated from more than 700K publications that consists of over 200K instances and 100K gene-disease pairs. Each instance consists of the sentence from which the GDA was extracted, the corresponding GDA, and the information about the gene-disease pair. Conclusions TBGA is amongst the largest datasets for GDA extraction. We have evaluated state-of-the-art models for GDA extraction on TBGA, showing that it is a challenging and well-suited dataset for the task. We made the dataset publicly available to foster the development of state-of-the-art BioRE models for GDA extraction. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04646-6.
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Affiliation(s)
- Stefano Marchesin
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Padova, Italy
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Taha K, Davuluri R, Yoo P, Spencer J. Personizing the prediction of future susceptibility to a specific disease. PLoS One 2021; 16:e0243127. [PMID: 33406077 PMCID: PMC7787538 DOI: 10.1371/journal.pone.0243127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 11/17/2020] [Indexed: 01/22/2023] Open
Abstract
A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.
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Affiliation(s)
- Kamal Taha
- Department of Electrical and Computer Science, Khalifa University, Abu Dhabi, UAE
- * E-mail:
| | - Ramana Davuluri
- Department of Biomedical Informatics, School of Medicine and College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York, United States of America
| | - Paul Yoo
- Department of Computer Science & Information Systems, University of London, Birkbeck College, London, United Kingdom
| | - Jesse Spencer
- Department of Pathology, University of Utah, Salt Lake City, Utah, United States of America
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Nicholson DN, Greene CS. Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J 2020; 18:1414-1428. [PMID: 32637040 PMCID: PMC7327409 DOI: 10.1016/j.csbj.2020.05.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/31/2022] Open
Abstract
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.
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Affiliation(s)
- David N. Nicholson
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, United States
| | - Casey S. Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, United States
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Gu J, Sun F, Qian L, Zhou G. Chemical-induced disease relation extraction via attention-based distant supervision. BMC Bioinformatics 2019; 20:403. [PMID: 31331263 PMCID: PMC6647285 DOI: 10.1186/s12859-019-2884-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 05/08/2019] [Indexed: 11/24/2022] Open
Abstract
Background Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment. Results We present an attention-based distant supervision paradigm for the BioCreative-V CDR extraction task. Training examples at both intra- and inter-sentence levels are generated automatically from the Comparative Toxicogenomics Database (CTD) without any human intervention. An attention-based neural network and a stacked auto-encoder network are applied respectively to induce learning models and extract relations at both levels. After merging the results of both levels, the document-level CDRs can be finally extracted. It achieves the precision/recall/F1-score of 60.3%/73.8%/66.4%, outperforming the state-of-the-art supervised learning systems without using any annotated corpus. Conclusion Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features simultaneously is effective in attention-based distantly supervised learning.
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Affiliation(s)
- Jinghang Gu
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.,Big Data Group, Baidu Inc., Beijing, China
| | - Fuqing Sun
- Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Longhua Qian
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.
| | - Guodong Zhou
- Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
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Bhasuran B, Natarajan J. Automatic extraction of gene-disease associations from literature using joint ensemble learning. PLoS One 2018; 13:e0200699. [PMID: 30048465 PMCID: PMC6061985 DOI: 10.1371/journal.pone.0200699] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 07/02/2018] [Indexed: 12/26/2022] Open
Abstract
A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down. In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases. By developing automatic extraction of gene-disease associations from the literature using joint ensemble learning we addressed this problem from a text mining perspective. In the proposed work, we employ a supervised machine learning approach in which a rich feature set covering conceptual, syntax and semantic properties jointly learned with word embedding are trained using ensemble support vector machine for extracting gene-disease relations from four gold standard corpora. Upon evaluating the machine learning approach shows promised results of 85.34%, 83.93%,87.39% and 85.57% of F-measure on EUADR, GAD, CoMAGC and PolySearch corpora respectively. We strongly believe that the presented novel approach combining rich syntax and semantic feature set with domain-specific word embedding through ensemble support vector machines evaluated on four gold standard corpora can act as a new baseline for future works in gene-disease relation extraction from literature.
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Affiliation(s)
- Balu Bhasuran
- DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India
| | - Jeyakumar Natarajan
- DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India
- Data mining and Text mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India
- * E-mail:
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Gu J, Sun F, Qian L, Zhou G. Chemical-induced disease relation extraction via convolutional neural network. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3098440. [PMID: 28415073 PMCID: PMC5467558 DOI: 10.1093/database/bax024] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/01/2017] [Indexed: 01/08/2023]
Abstract
This article describes our work on the BioCreative-V chemical–disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL:http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/
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Affiliation(s)
- Jinghang Gu
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
| | - Fuqing Sun
- Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 17 Qihelou Street, Beijing, China
| | - Longhua Qian
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
| | - Guodong Zhou
- School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China
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Chen CC, Ho CL. StemTextSearch: Stem cell gene database with evidence from abstracts. J Biomed Inform 2017; 69:150-159. [PMID: 28315408 DOI: 10.1016/j.jbi.2017.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/08/2017] [Accepted: 03/10/2017] [Indexed: 11/29/2022]
Abstract
BACKGROUND Previous studies have used many methods to find biomarkers in stem cells, including text mining, experimental data and image storage. However, no text-mining methods have yet been developed which can identify whether a gene plays a positive or negative role in stem cells. DESCRIPTION StemTextSearch identifies the role of a gene in stem cells by using a text-mining method to find combinations of gene regulation, stem-cell regulation and cell processes in the same sentences of biomedical abstracts. CONCLUSIONS The dataset includes 5797 genes, with 1534 genes having positive roles in stem cells, 1335 genes having negative roles, 1654 genes with both positive and negative roles, and 1274 with an uncertain role. The precision of gene role in StemTextSearch is 0.66, and the recall is 0.78. StemTextSearch is a web-based engine with queries that specify (i) gene, (ii) category of stem cell, (iii) gene role, (iv) gene regulation, (v) cell process, (vi) stem-cell regulation, and (vii) species. StemTextSearch is available through http://bio.yungyun.com.tw/StemTextSearch.aspx.
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Affiliation(s)
- Chou-Cheng Chen
- Department of Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
| | - Chung-Liang Ho
- Department of Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan; Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan; Institute of Molecular Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.
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Peng Y, Wei CH, Lu Z. Improving chemical disease relation extraction with rich features and weakly labeled data. J Cheminform 2016; 8:53. [PMID: 28316651 PMCID: PMC5054544 DOI: 10.1186/s13321-016-0165-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 09/28/2016] [Indexed: 01/08/2023] Open
Abstract
Background Due to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations. Results We propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data. Conclusions Our text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.
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Affiliation(s)
- Yifan Peng
- National Center for Biotechnology Information, Bethesda, MD 20894 USA ; Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, Bethesda, MD 20894 USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, Bethesda, MD 20894 USA
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11
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Verspoor KM, Heo GE, Kang KY, Song M. Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts. BMC Med Inform Decis Mak 2016; 16 Suppl 1:68. [PMID: 27454860 PMCID: PMC4959367 DOI: 10.1186/s12911-016-0294-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [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 The Variome corpus, a small collection of published articles about inherited colorectal cancer, includes annotations of 11 entity types and 13 relation types related to the curation of the relationship between genetic variation and disease. Due to the richness of these annotations, the corpus provides a good testbed for evaluation of biomedical literature information extraction systems. METHODS In this paper, we focus on assessing performance on extracting the relations in the corpus, using gold standard entities as a starting point, to establish a baseline for extraction of relations important for extraction of genetic variant information from the literature. We test the application of the Public Knowledge Discovery Engine for Java (PKDE4J) system, a natural language processing system designed for information extraction of entities and relations in text, on the relation extraction task using this corpus. RESULTS For the relations which are attested at least 100 times in the Variome corpus, we realise a performance ranging from 0.78-0.84 Precision-weighted F-score, depending on the relation. We find that the PKDE4J system adapted straightforwardly to the range of relation types represented in the corpus; some extensions to the original methodology were required to adapt to the multi-relational classification context. The results are competitive with state-of-the-art relation extraction performance on more heavily studied corpora, although the analysis shows that the Recall of a co-occurrence baseline outweighs the benefit of improved Precision for many relations, indicating the value of simple semantic constraints on relations. CONCLUSIONS This work represents the first attempt to apply relation extraction methods to the Variome corpus. The results demonstrate that automated methods have good potential to structure the information expressed in the published literature related to genetic variants, connecting mutations to genes, diseases, and patient cohorts. Further development of such approaches will facilitate more efficient biocuration of genetic variant information into structured databases, leveraging the knowledge embedded in the vast publication literature.
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Affiliation(s)
- Karin M Verspoor
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
| | - Go Eun Heo
- Department of Library and Information Science, Yonsei University, Seoul, Korea
| | - Keun Young Kang
- Department of Library and Information Science, Yonsei University, Seoul, Korea
| | - Min Song
- Department of Library and Information Science, Yonsei University, Seoul, Korea.
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Song M, Kim WC, Lee D, Heo GE, Kang KY. PKDE4J: Entity and relation extraction for public knowledge discovery. J Biomed Inform 2015; 57:320-32. [PMID: 26277115 DOI: 10.1016/j.jbi.2015.08.008] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 07/20/2015] [Accepted: 08/06/2015] [Indexed: 11/18/2022]
Abstract
Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.
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Affiliation(s)
- Min Song
- Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea.
| | - Won Chul Kim
- Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea.
| | - Dahee Lee
- Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea.
| | - Go Eun Heo
- Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea.
| | - Keun Young Kang
- Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea.
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Lee HJ, Dang TC, Lee H, Park JC. OncoSearch: cancer gene search engine with literature evidence. Nucleic Acids Res 2014; 42:W416-21. [PMID: 24813447 PMCID: PMC4086113 DOI: 10.1093/nar/gku368] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In order to identify genes that are involved in oncogenesis and to understand how such genes affect cancers, abnormal gene expressions in cancers are actively studied. For an efficient access to the results of such studies that are reported in biomedical literature, the relevant information is accumulated via text-mining tools and made available through the Web. However, current Web tools are not yet tailored enough to allow queries that specify how a cancer changes along with the change in gene expression level, which is an important piece of information to understand an involved gene's role in cancer progression or regression. OncoSearch is a Web-based engine that searches Medline abstracts for sentences that mention gene expression changes in cancers, with queries that specify (i) whether a gene expression level is up-regulated or down-regulated, (ii) whether a certain type of cancer progresses or regresses along with such gene expression change and (iii) the expected role of the gene in the cancer. OncoSearch is available through http://oncosearch.biopathway.org.
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Affiliation(s)
- Hee-Jin Lee
- Department of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Tien Cuong Dang
- Department of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Hyunju Lee
- School of Information and Communications, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 500-712, Republic of Korea
| | - Jong C Park
- Department of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
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