1
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Gonzalez-Hernandez G, Krallinger M, Muñoz M, Rodriguez-Esteban R, Uzuner Ö, Hirschman L. Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers. Database (Oxford) 2022; 2022:6682867. [PMID: 36050787 PMCID: PMC9436770 DOI: 10.1093/database/baac071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/08/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022]
Abstract
Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.
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Affiliation(s)
- Graciela Gonzalez-Hernandez
- Department of Computational Biomedicine, Cedars-Sinai Medical Center , 700 N. San Vicente Blvd., West Hollywood, CA 90069, USA
| | - Martin Krallinger
- Life Sciences—Text Mining, Barcelona Supercomputing Center , Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Monica Muñoz
- Division of Pharmacovigilance, Office of Surveillance and Epidemiology, Center of Drug Evaluation and Research, FDA , 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Raul Rodriguez-Esteban
- Roche Innovation Center Basel, Roche Pharmaceuticals , Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Özlem Uzuner
- Information Sciences and Technology, George Mason University , 4400 University Dr, Fairfax, VA 22030, USA
| | - Lynette Hirschman
- MITRE Labs, The MITRE Corporation , 202 Burlington Rd., Bedford, MA 01730, USA
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2
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Nassar M, Rogers AB, Talo' F, Sanchez S, Shafique Z, Finn RD, McEntyre J. A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications. Gigascience 2022; 11:giac077. [PMID: 35950838 PMCID: PMC9366992 DOI: 10.1093/gigascience/giac077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/13/2022] [Accepted: 07/12/2022] [Indexed: 11/17/2022] Open
Abstract
Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API.
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Affiliation(s)
- Maaly Nassar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Current affiliation: SciBite - an Elsevier Company, Wellcome Genome Campus, Hinxton, Cambridge CB10 1DR, UK
| | - Alexander B Rogers
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Francesco Talo'
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Santiago Sanchez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Zunaira Shafique
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Johanna McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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3
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Lee K, Wei CH, Lu Z. Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature. Brief Bioinform 2021; 22:bbaa142. [PMID: 32770181 PMCID: PMC8138883 DOI: 10.1093/bib/bbaa142] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/07/2020] [Accepted: 06/25/2020] [Indexed: 12/28/2022] Open
Abstract
MOTIVATION To obtain key information for personalized medicine and cancer research, clinicians and researchers in the biomedical field are in great need of searching genomic variant information from the biomedical literature now than ever before. Due to the various written forms of genomic variants, however, it is difficult to locate the right information from the literature when using a general literature search system. To address the difficulty of locating genomic variant information from the literature, researchers have suggested various solutions based on automated literature-mining techniques. There is, however, no study for summarizing and comparing existing tools for genomic variant literature mining in terms of how to search easily for information in the literature on genomic variants. RESULTS In this article, we systematically compared currently available genomic variant recognition and normalization tools as well as the literature search engines that adopted these literature-mining techniques. First, we explain the problems that are caused by the use of non-standard formats of genomic variants in the PubMed literature by considering examples from the literature and show the prevalence of the problem. Second, we review literature-mining tools that address the problem by recognizing and normalizing the various forms of genomic variants in the literature and systematically compare them. Third, we present and compare existing literature search engines that are designed for a genomic variant search by using the literature-mining techniques. We expect this work to be helpful for researchers who seek information about genomic variants from the literature, developers who integrate genomic variant information from the literature and beyond.
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Affiliation(s)
- Kyubum Lee
- National Center for Biotechnology Information
| | | | - Zhiyong Lu
- National Center for Biotechnology Information
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4
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Shi W, Chen X, Deng L. A Review of Recent Developments and Progress in Computational Drug Repositioning. Curr Pharm Des 2021; 26:3059-3068. [PMID: 31951162 DOI: 10.2174/1381612826666200116145559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/09/2020] [Indexed: 12/27/2022]
Abstract
Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.
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Affiliation(s)
- Wanwan Shi
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xuegong Chen
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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5
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Karadeniz İ, Özgür A. Linking entities through an ontology using word embeddings and syntactic re-ranking. BMC Bioinformatics 2019; 20:156. [PMID: 30917789 PMCID: PMC6437991 DOI: 10.1186/s12859-019-2678-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 02/13/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although there is an enormous number of textual resources in the biomedical domain, currently, manually curated resources cover only a small part of the existing knowledge. The vast majority of these information is in unstructured form which contain nonstandard naming conventions. The task of named entity recognition, which is the identification of entity names from text, is not adequate without a standardization step. Linking each identified entity mention in text to an ontology/dictionary concept is an essential task to make sense of the identified entities. This paper presents an unsupervised approach for the linking of named entities to concepts in an ontology/dictionary. We propose an approach for the normalization of biomedical entities through an ontology/dictionary by using word embeddings to represent semantic spaces, and a syntactic parser to give higher weight to the most informative word in the named entity mentions. RESULTS We applied the proposed method to two different normalization tasks: the normalization of bacteria biotope entities through the Onto-Biotope ontology and the normalization of adverse drug reaction entities through the Medical Dictionary for Regulatory Activities (MedDRA). The proposed method achieved a precision score of 65.9%, which is 2.9 percentage points above the state-of-the-art result on the BioNLP Shared Task 2016 Bacteria Biotope test data and a macro-averaged precision score of 68.7% on the Text Analysis Conference 2017 Adverse Drug Reaction test data. CONCLUSIONS The core contribution of this paper is a syntax-based way of combining the individual word vectors to form vectors for the named entity mentions and ontology concepts, which can then be used to measure the similarity between them. The proposed approach is unsupervised and does not require labeled data, making it easily applicable to different domains.
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Affiliation(s)
- İlknur Karadeniz
- Department of Computer Engineering, Boğaziçi University, İstanbul, 34342, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boğaziçi University, İstanbul, 34342, Turkey.
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6
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Pletscher-Frankild S, Jensen LJ. Design, implementation, and operation of a rapid, robust named entity recognition web service. J Cheminform 2019; 11:19. [PMID: 30850898 PMCID: PMC6419787 DOI: 10.1186/s13321-019-0344-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 03/04/2019] [Indexed: 01/09/2023] Open
Abstract
Most BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision and recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. For about a decade, we have run named entity recognition (NER) web services, which are designed to be efficient, implemented using a multi-threaded queueing system to robustly handle many simultaneous requests, and hosted at a supercomputer facility. To participate in this new task, we extended the existing NER tagging service with support for the BeCalm API. The tagger suffered no downtime during the challenge and, as in earlier tests, proved to be highly efficient, consistently processing requests of 5000 abstracts in less than half a minute. In fact, the majority of this time was spent not on the NER task but rather on retrieving the document texts from the challenge servers. The latter was found to be the main bottleneck even when hosting a copy of the tagging service on a Raspberry Pi 3, showing that local document storage or caching would be desirable features to include in future revisions of the API standard.
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Affiliation(s)
- Sune Pletscher-Frankild
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Intomics A/S, Lyngby, Denmark
| | - Lars Juhl Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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7
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Popovski G, Seljak BK, Eftimov T. FoodBase corpus: a new resource of annotated food entities. Database (Oxford) 2019; 2019:baz121. [PMID: 31682732 PMCID: PMC6827550 DOI: 10.1093/database/baz121] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/05/2019] [Accepted: 09/17/2019] [Indexed: 12/24/2022]
Abstract
The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora related to different biomedical entities such as genes, phenotypes, drugs, diseases and chemical entities. These are needed to develop named-entity recognition (NER) models that are used for extracting entities from text and finding their relations. However, to the best of our knowledge, there are limited annotated corpora that provide information about food entities despite food and dietary management being an essential public health issue. Hence, we developed a new annotated corpus of food entities, named FoodBase. It was constructed using recipes extracted from Allrecipes, which is currently the largest food-focused social network. The recipes were selected from five categories: 'Appetizers and Snacks', 'Breakfast and Lunch', 'Dessert', 'Dinner' and 'Drinks'. Semantic tags used for annotating food entities were selected from the Hansard corpus. To extract and annotate food entities, we applied a rule-based food NER method called FoodIE. Since FoodIE provides a weakly annotated corpus, by manually evaluating the obtained results on 1000 recipes, we created a gold standard of FoodBase. It consists of 12 844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21 790 recipes. It consists of 274 053 food entity annotations, 13 079 of which are unique. The FoodBase corpus is necessary for developing corpus-based NER models for food science, as a new benchmark dataset for machine learning tasks such as multi-class classification, multi-label classification and hierarchical multi-label classification. FoodBase can be used for detecting semantic differences/similarities between food concepts, and after all we believe that it will open a new path for learning food embedding space that can be used in predictive studies.
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Affiliation(s)
- Gorjan Popovski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, ul.Rudzer Boshkovikj 16, 1000 Skopje, Macedonia
- Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Barbara Koroušić Seljak
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Tome Eftimov
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford 94305 CA, USA
- Center for Population Health Sciences, Stanford University, 450 Serra Mall, Stanford 94305 CA, USA
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8
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Abstract
PubMed contains more than 27 million documents, and this number is growing at an estimated 4% per year. Even within specialized topics, it is no longer possible for a researcher to read any field in its entirety, and thus nobody has a complete picture of the scientific knowledge in any given field at any time. Text mining provides a means to automatically read this corpus and to extract the relations found therein as structured information. Having data in a structured format is a huge boon for computational efforts to access, cross reference, and mine the data stored therein. This is increasingly useful as biological research is becoming more focused on systems and multi-omics integration. This chapter provides an overview of the steps that are required for text mining: tokenization, named entity recognition, normalization, event extraction, and benchmarking. It discusses a variety of approaches to these tasks and then goes into detail on how to prepare data for use specifically with the JensenLab tagger. This software uses a dictionary-based approach and provides the text mining evidence for STRING and several other databases.
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Affiliation(s)
- Helen V Cook
- School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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9
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Popovski G, Seljak BK, Eftimov T. FoodBase corpus: a new resource of annotated food entities. Database (Oxford) 2019; 2019:5611291. [PMID: 31682732 DOI: 10.1093/database/baz121(2019)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Revised: 07/05/2019] [Accepted: 09/17/2019] [Indexed: 05/28/2023]
Abstract
The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora related to different biomedical entities such as genes, phenotypes, drugs, diseases and chemical entities. These are needed to develop named-entity recognition (NER) models that are used for extracting entities from text and finding their relations. However, to the best of our knowledge, there are limited annotated corpora that provide information about food entities despite food and dietary management being an essential public health issue. Hence, we developed a new annotated corpus of food entities, named FoodBase. It was constructed using recipes extracted from Allrecipes, which is currently the largest food-focused social network. The recipes were selected from five categories: 'Appetizers and Snacks', 'Breakfast and Lunch', 'Dessert', 'Dinner' and 'Drinks'. Semantic tags used for annotating food entities were selected from the Hansard corpus. To extract and annotate food entities, we applied a rule-based food NER method called FoodIE. Since FoodIE provides a weakly annotated corpus, by manually evaluating the obtained results on 1000 recipes, we created a gold standard of FoodBase. It consists of 12 844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21 790 recipes. It consists of 274 053 food entity annotations, 13 079 of which are unique. The FoodBase corpus is necessary for developing corpus-based NER models for food science, as a new benchmark dataset for machine learning tasks such as multi-class classification, multi-label classification and hierarchical multi-label classification. FoodBase can be used for detecting semantic differences/similarities between food concepts, and after all we believe that it will open a new path for learning food embedding space that can be used in predictive studies.
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Affiliation(s)
- Gorjan Popovski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, ul.Rudzer Boshkovikj 16, 1000 Skopje, Macedonia
- Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Barbara Koroušić Seljak
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Tome Eftimov
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford 94305 CA, USA
- Center for Population Health Sciences, Stanford University, 450 Serra Mall, Stanford 94305 CA, USA
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10
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Kondratova M, Sompairac N, Barillot E, Zinovyev A, Kuperstein I. Signalling maps in cancer research: construction and data analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4964960. [PMID: 29688383 PMCID: PMC5890450 DOI: 10.1093/database/bay036] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 03/19/2018] [Indexed: 12/22/2022]
Abstract
Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.
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Affiliation(s)
- Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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11
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Eftimov T, Koroušić Seljak B, Korošec P. A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations. PLoS One 2017. [PMID: 28644863 PMCID: PMC5482438 DOI: 10.1371/journal.pone.0179488] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
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Affiliation(s)
- Tome Eftimov
- Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
- * E-mail:
| | | | - Peter Korošec
- Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
- Faculty of Mathematics, Natural Science and Information Technologies, Koper, Slovenia
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12
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Abstract
The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.
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13
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Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A. Information Retrieval and Text Mining Technologies for Chemistry. Chem Rev 2017; 117:7673-7761. [PMID: 28475312 DOI: 10.1021/acs.chemrev.6b00851] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre , C/Melchor Fernández Almagro 3, Madrid E-28029, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Anália Lourenço
- ESEI - Department of Computer Science, University of Vigo , Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense E-32004, Spain.,Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia) , Campus Universitario Lagoas-Marcosende, Vigo E-36310, Spain.,CEB-Centre of Biological Engineering, University of Minho , Campus de Gualtar, Braga 4710-057, Portugal
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Alfonso Valencia
- Life Science Department, Barcelona Supercomputing Centre (BSC-CNS) , C/Jordi Girona, 29-31, Barcelona E-08034, Spain.,Joint BSC-IRB-CRG Program in Computational Biology, Parc Científic de Barcelona , C/ Baldiri Reixac 10, Barcelona E-08028, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig de Lluís Companys 23, Barcelona E-08010, Spain
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14
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Madan S, Hodapp S, Senger P, Ansari S, Szostak J, Hoeng J, Peitsch M, Fluck J. The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track. Database (Oxford) 2016; 2016:baw136. [PMID: 27694210 PMCID: PMC5045868 DOI: 10.1093/database/baw136] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 08/26/2016] [Accepted: 08/30/2016] [Indexed: 11/14/2022]
Abstract
Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/.
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Affiliation(s)
- Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sven Hodapp
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Philipp Senger
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sam Ansari
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Justyna Szostak
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Manuel Peitsch
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
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Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, Wiegers J, Wiegers TC, Mattingly CJ. The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Res 2016; 45:D972-D978. [PMID: 27651457 PMCID: PMC5210612 DOI: 10.1093/nar/gkw838] [Citation(s) in RCA: 386] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 09/09/2016] [Indexed: 12/19/2022] Open
Abstract
The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.
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Affiliation(s)
- Allan Peter Davis
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Cynthia J Grondin
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Robin J Johnson
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Daniela Sciaky
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Benjamin L King
- Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME 04672, USA
| | - Roy McMorran
- Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME 04672, USA
| | - Jolene Wiegers
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Thomas C Wiegers
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Carolyn J Mattingly
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.,Center for Human Health and the Environment, North Carolina State University, Raleigh, NC 27695, USA
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Névéol A, Cohen KB, Grouin C, Hamon T, Lavergne T, Kelly L, Goeuriot L, Rey G, Robert A, Tannier X, Zweigenbaum P. Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016. CEUR WORKSHOP PROCEEDINGS 2016; 1609:28-42. [PMID: 29308065 PMCID: PMC5756095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with named entity recognition and normalization in French narratives, as offered in CLEF eHealth 2015. Named entity recognition involved ten types of entities including disorders that were defined according to Semantic Groups in the Unified Medical Language System® (UMLS®), which was also used for normalizing the entities. In addition, we introduced a large-scale classification task in French death certificates, which consisted of extracting causes of death as coded in the International Classification of Diseases, tenth revision (ICD10). Participant systems were evaluated against a blind reference standard of 832 titles of scientific articles indexed in MEDLINE, 4 drug monographs published by the European Medicines Agency (EMEA) and 27,850 death certificates using Precision, Recall and F-measure. In total, seven teams participated, including five in the entity recognition and normalization task, and five in the death certificate coding task. Three teams submitted their systems to our newly offered reproducibility track. For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For entity normalization, the highest performance was achieved on the MEDLINE corpus, with an overall F-measure of 0.552. For death certificate coding, the highest performance was 0.848 F-measure.
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Affiliation(s)
| | - K Bretonnel Cohen
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
- University of Colorado, USA
| | - Cyril Grouin
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
| | - Thierry Hamon
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
- Université Paris Nord, Villetaneuse, France
| | - Thomas Lavergne
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
- Univ. Paris-Sud, Orsay, France
| | - Liadh Kelly
- ADAPT Centre, Trinity College, Dublin, Ireland
| | | | | | | | - Xavier Tannier
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
- Univ. Paris-Sud, Orsay, France
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Fluck J, Madan S, Ansari S, Kodamullil AT, Karki R, Rastegar-Mojarad M, Catlett NL, Hayes W, Szostak J, Hoeng J, Peitsch M. Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL). DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw113. [PMID: 27554092 PMCID: PMC4995071 DOI: 10.1093/database/baw113] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 07/07/2016] [Indexed: 01/21/2023]
Abstract
Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task. Database URL:http://wiki.openbel.org/display/BIOC/Datasets
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Affiliation(s)
- Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sam Ansari
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Alpha T Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | | | | | - William Hayes
- Selventa, One Alewife Center, Cambridge, MA 02140, USA
| | - Justyna Szostak
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Manuel Peitsch
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
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Almeida H, Meurs MJ, Kosseim L, Tsang A. Data Sampling and Supervised Learning for HIV Literature Screening. IEEE Trans Nanobioscience 2016; 15:354-361. [DOI: 10.1109/tnb.2016.2565481] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:139-166. [PMID: 27807747 DOI: 10.1007/978-981-10-1503-8_7] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.
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Wei CH, Leaman R, Lu Z. SimConcept: a hybrid approach for simplifying composite named entities in biomedical text. IEEE J Biomed Health Inform 2015; 19:1385-91. [PMID: 25879978 PMCID: PMC4543296 DOI: 10.1109/jbhi.2015.2422651] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
One particular challenge in biomedical named entity recognition (NER) and normalization is the identification and resolution of composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Previous NER and normalization studies have either ignored composite mentions, used simple ad hoc rules, or only handled coordination ellipsis, making a robust approach for handling multitype composite mentions greatly needed. To this end, we propose a hybrid method integrating a machine-learning model with a pattern identification strategy to identify the individual components of each composite mention. Our method, which we have named SimConcept, is the first to systematically handle many types of composite mentions. The technique achieves high performance in identifying and resolving composite mentions for three key biological entities: genes (90.42% in F-measure), diseases (86.47% in F-measure), and chemicals (86.05% in F-measure). Furthermore, our results show that using our SimConcept method can subsequently improve the performance of gene and disease concept recognition and normalization. SimConcept is available for download at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/SimConcept/.
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Hsu YY, Kao HY. Curatable Named-Entity Recognition Using Semantic Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:785-792. [PMID: 26357317 DOI: 10.1109/tcbb.2014.2366770] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Named-entity recognition (NER) plays an important role in the development of biomedical databases. However, the existing NER tools produce multifarious named-entities which may result in both curatable and non-curatable markers. To facilitate biocuration with a straightforward approach, classifying curatable named-entities is helpful with regard to accelerating the biocuration workflow. Co-occurrence Interaction Nexus with Named-entity Recognition (CoINNER) is a web-based tool that allows users to identify genes, chemicals, diseases, and action term mentions in the Comparative Toxicogenomic Database (CTD). To further discover interactions, CoINNER uses multiple advanced algorithms to recognize the mentions in the BioCreative IV CTD Track. CoINNER is developed based on a prototype system that annotated gene, chemical, and disease mentions in PubMed abstracts at BioCreative 2012 Track I (literature triage). We extended our previous system in developing CoINNER. The pre-tagging results of CoINNER were developed based on the state-of-the-art named entity recognition tools in BioCreative III. Next, a method based on conditional random fields (CRFs) is proposed to predict chemical and disease mentions in the articles. Finally, action term mentions were collected by latent Dirichlet allocation (LDA). At the BioCreative IV CTD Track, the best F-measures reached for gene/protein, chemical/drug and disease NER were 54 percent while CoINNER achieved a 61.5 percent F-measure. System URL: http://ikmbio.csie.ncku.edu.tw/coinner/ introduction.htm.
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Srinivasan P, Zhang XN, Bouten R, Chang C. Ferret: a sentence-based literature scanning system. BMC Bioinformatics 2015; 16:198. [PMID: 26091670 PMCID: PMC4474359 DOI: 10.1186/s12859-015-0630-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 05/29/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The rapid pace of bioscience research makes it very challenging to track relevant articles in one's area of interest. MEDLINE, a primary source for biomedical literature, offers access to more than 20 million citations with three-quarters of a million new ones added each year. Thus it is not surprising to see active research in building new document retrieval and sentence retrieval systems. We present Ferret, a prototype retrieval system, designed to retrieve and rank sentences (and their documents) conveying gene-centric relationships of interest to a scientist. The prototype has several features. For example, it is designed to handle gene name ambiguity and perform query expansion. Inputs can be a list of genes with an optional list of keywords. Sentences are retrieved across species but the species discussed in the records are identified. Results are presented in the form of a heat map and sentences corresponding to specific cells of the heat map may be selected for display. Ferret is designed to assist bio scientists at different stages of research from early idea exploration to advanced analysis of results from bench experiments. RESULTS Three live case studies in the field of plant biology are presented related to Arabidopsis thaliana. The first is to discover genes that may relate to the phenotype of open immature flower in Arabidopsis. The second case is about finding associations reported between ethylene signaling and a set of 300+ Arabidopsis genes. The third case is on searching for potential gene targets of an Arabidopsis transcription factor hypothesized to be involved in plant stress responses. Ferret was successful in finding valuable information in all three cases. In the first case the bZIP family of genes was identified. In the second case sentences indicating relevant associations were found in other species such as potato and jasmine. In the third sentences led to new research questions about the plant hormone salicylic acid. CONCLUSIONS Ferret successfully retrieved relevant gene-centric sentences from PubMed records. The three case studies demonstrate end user satisfaction with the system.
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Affiliation(s)
| | - Xiao-Ning Zhang
- Department of Biology, St Bonaventure University, St Bonaventure, NY, USA.
| | - Roxane Bouten
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.
| | - Caren Chang
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.
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Mandloi S, Chakrabarti S. PALM-IST: Pathway Assembly from Literature Mining--an Information Search Tool. Sci Rep 2015; 5:10021. [PMID: 25989388 PMCID: PMC4437304 DOI: 10.1038/srep10021] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2014] [Accepted: 02/18/2015] [Indexed: 01/09/2023] Open
Abstract
Manual curation of biomedical literature has become extremely tedious process due to its exponential growth in recent years. To extract meaningful information from such large and unstructured text, newer and more efficient mining tool is required. Here, we introduce PALM-IST, a computational platform that not only allows users to explore biomedical abstracts using keyword based text mining but also extracts biological entity (e.g., gene/protein, drug, disease, biological processes, cellular component, etc.) information from the extracted text and subsequently mines various databases to provide their comprehensive inter-relation (e.g., interaction, expression, etc.). PALM-IST constructs protein interaction network and pathway information data relevant to the text search using multiple data mining tools and assembles them to create a meta-interaction network. It also analyzes scientific collaboration by extraction and creation of "co-authorship network," for a given search context. Hence, this useful combination of literature and data mining provided in PALM-IST can be used to extract novel protein-protein interaction (PPI), to generate meta-pathways and further to identify key crosstalk and bottleneck proteins. PALM-IST is available at www.hpppi.iicb.res.in/ctm.
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Affiliation(s)
- Sapan Mandloi
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata 700032, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata 700032, India
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24
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Huang CC, Lu Z. Community challenges in biomedical text mining over 10 years: success, failure and the future. Brief Bioinform 2015; 17:132-44. [PMID: 25935162 DOI: 10.1093/bib/bbv024] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Indexed: 11/13/2022] Open
Abstract
One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations.
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Khare R, Good BM, Leaman R, Su AI, Lu Z. Crowdsourcing in biomedicine: challenges and opportunities. Brief Bioinform 2015; 17:23-32. [PMID: 25888696 DOI: 10.1093/bib/bbv021] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine.
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Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform 2015; 17:2-12. [PMID: 25832646 DOI: 10.1093/bib/bbv020] [Citation(s) in RCA: 338] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Indexed: 12/26/2022] Open
Abstract
Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
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Orchard S, Hermjakob H. Shared resources, shared costs--leveraging biocuration resources. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav009. [PMID: 25776020 PMCID: PMC4360620 DOI: 10.1093/database/bav009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The manual curation of the information in biomedical resources is an expensive task. This article argues the value of this approach in comparison with other apparently less costly options, such as automated annotation or text-mining, then discusses ways in which databases can make cost savings by sharing infrastructure and tool development. Sharing curation effort is a model already being adopted by several data resources. Approaches taken by two of these, the Gene Ontology annotation effort and the IntAct molecular interaction database, are reviewed in more detail. These models help to ensure long-term persistence of curated data and minimizes redundant development of resources by multiple disparate groups. Database URL:http://www.ebi.ac.uk/intact and http://www.ebi.ac.uk/GOA/
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Affiliation(s)
- Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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28
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Bravo À, Piñero J, Queralt-Rosinach N, Rautschka M, Furlong LI. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinformatics 2015; 16:55. [PMID: 25886734 PMCID: PMC4466840 DOI: 10.1186/s12859-015-0472-9] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 01/19/2015] [Indexed: 11/23/2022] Open
Abstract
Background Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. Results By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. Conclusions BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0472-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Àlex Bravo
- Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Núria Queralt-Rosinach
- Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Michael Rautschka
- Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), IMIM, DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.
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29
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Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A. CHEMDNER: The drugs and chemical names extraction challenge. J Cheminform 2015; 7:S1. [PMID: 25810766 PMCID: PMC4331685 DOI: 10.1186/1758-2946-7-s1-s1] [Citation(s) in RCA: 128] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
| | - Florian Leitner
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politecnica de Madrid, Calle Ramiro de Maeztu, 7, Madrid, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Center for Applied Medical Research (CIMA), University of Navarra, Avenida de Pio XII, 55, Pamplona, Spain
| | - Miguel Vazquez
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Center for Applied Medical Research (CIMA), University of Navarra, Avenida de Pio XII, 55, Pamplona, Spain
| | - Alfonso Valencia
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
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30
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Krallinger M, Rabal O, Leitner F, Vazquez M, Salgado D, Lu Z, Leaman R, Lu Y, Ji D, Lowe DM, Sayle RA, Batista-Navarro RT, Rak R, Huber T, Rocktäschel T, Matos S, Campos D, Tang B, Xu H, Munkhdalai T, Ryu KH, Ramanan SV, Nathan S, Žitnik S, Bajec M, Weber L, Irmer M, Akhondi SA, Kors JA, Xu S, An X, Sikdar UK, Ekbal A, Yoshioka M, Dieb TM, Choi M, Verspoor K, Khabsa M, Giles CL, Liu H, Ravikumar KE, Lamurias A, Couto FM, Dai HJ, Tsai RTH, Ata C, Can T, Usié A, Alves R, Segura-Bedmar I, Martínez P, Oyarzabal J, Valencia A. The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform 2015; 7:S2. [PMID: 25810773 PMCID: PMC4331692 DOI: 10.1186/1758-2946-7-s1-s2] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Florian Leitner
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politecnica de Madrid, Madrid, Spain
| | - Miguel Vazquez
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - David Salgado
- Faculte de Medecine La Timone, Marseille, Marseille, France
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Institutes of Health, Bethesda, USA
| | - Robert Leaman
- National Center for Biotechnology Information (NCBI), National Institutes of Health, Bethesda, USA
| | - Yanan Lu
- Natural Language Processing Lab, Wuhan University, Wuhan, Hubei, PR China
| | - Donghong Ji
- Natural Language Processing Lab, Wuhan University, Wuhan, Hubei, PR China
| | - Daniel M Lowe
- NextMove Software Ltd, Innovation Centre, Unit 23, Science Park, Milton Road, Cambridge, UK
| | - Roger A Sayle
- NextMove Software Ltd, Innovation Centre, Unit 23, Science Park, Milton Road, Cambridge, UK
| | | | - Rafal Rak
- National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester, UK
| | - Torsten Huber
- Humboldt-Universität zu Berlin, Knowledge Management in Bioinformatics, Berlin, Germany
| | - Tim Rocktäschel
- Department of Computer Science, University College London, London, UK
| | - Sérgio Matos
- IEETA/DETI, University of Aveiro, Campus Universitario de Santiago, Aveiro, Portugal
| | - David Campos
- IEETA/DETI, University of Aveiro, Campus Universitario de Santiago, Aveiro, Portugal
| | - Buzhou Tang
- Department of Computer Science, Harbin Institute of Technology, Shenzhen Graduate School Shenzhen, GuangDong, PR China
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, USA
| | - Tsendsuren Munkhdalai
- Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Keun Ho Ryu
- Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - SV Ramanan
- RelAgent Pvt Ltd, IIT Madras Research Park, Taramani, Chennai, India
| | - Senthil Nathan
- RelAgent Pvt Ltd, IIT Madras Research Park, Taramani, Chennai, India
| | - Slavko Žitnik
- Faculty of computer and information science, University of Ljubljana, Ljubljana, Slovenia
| | - Marko Bajec
- Faculty of computer and information science, University of Ljubljana, Ljubljana, Slovenia
| | | | | | - Saber A Akhondi
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Shuo Xu
- Information Technology Supporting Center, Institute of Scientific and Technical Information of China, Beijing, PR China
| | - Xin An
- School of Economics and Management, Beijing Forestry University, Beijing, PR China
| | - Utpal Kumar Sikdar
- Department of Computer Science and Engineering Indian institute of Technology, Patna, Bihar, India
| | - Asif Ekbal
- Department of Computer Science and Engineering Indian institute of Technology, Patna, Bihar, India
| | - Masaharu Yoshioka
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Thaer M Dieb
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Miji Choi
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
- National ICT Australia Victoria Research Laboratory, West Melbourne, Australia
| | - Madian Khabsa
- Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, USA
| | - C Lee Giles
- Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, USA
- Information Sciences and Technology, The Pennsylvania State University, Pennsylvania, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo College of Medicine, Rochester, USA
| | | | - Andre Lamurias
- LaSIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Francisco M Couto
- LaSIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Hong-Jie Dai
- Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Richard Tzong-Han Tsai
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Caglar Ata
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tolga Can
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Anabel Usié
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Lleida, Spain
- Departament d'Informatica i Enginyeria Industrial, Univesitat de Lleida, Lleida, Spain
| | - Rui Alves
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Lleida, Spain
| | | | - Paloma Martínez
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Alfonso Valencia
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
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31
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Munkhdalai T, Li M, Batsuren K, Park HA, Choi NH, Ryu KH. Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations. J Cheminform 2015; 7:S9. [PMID: 25810780 PMCID: PMC4331699 DOI: 10.1186/1758-2946-7-s1-s9] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. Results We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.
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Affiliation(s)
- Tsendsuren Munkhdalai
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Meijing Li
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Khuyagbaatar Batsuren
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Hyeon Ah Park
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Nak Hyeon Choi
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Keun Ho Ryu
- Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, South Korea
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32
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Van Auken K, Schaeffer ML, McQuilton P, Laulederkind SJF, Li D, Wang SJ, Hayman GT, Tweedie S, Arighi CN, Done J, Müller HM, Sternberg PW, Mao Y, Wei CH, Lu Z. BC4GO: a full-text corpus for the BioCreative IV GO task. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau074. [PMID: 25070993 PMCID: PMC4112614 DOI: 10.1093/database/bau074] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community. Database URL:http://www.biocreative.org/resources/corpora/bc-iv-go-task-corpus/.
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Affiliation(s)
- Kimberly Van Auken
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Mary L Schaeffer
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Peter McQuilton
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stanley J F Laulederkind
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Donghui Li
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Shur-Jen Wang
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - G Thomas Hayman
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Susan Tweedie
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Cecilia N Arighi
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - James Done
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Hans-Michael Müller
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Paul W Sternberg
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USAWormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Yuqing Mao
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Chih-Hsuan Wei
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- WormBase, Division of Biology, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, TAIR, Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA, National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
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Khare R, Wei CH, Mao Y, Leaman R, Lu Z. tmBioC: improving interoperability of text-mining tools with BioC. Database (Oxford) 2014; 2014:bau073. [PMID: 25062914 PMCID: PMC4110697 DOI: 10.1093/database/bau073] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 06/30/2014] [Accepted: 07/01/2014] [Indexed: 02/05/2023]
Abstract
The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/.
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Affiliation(s)
- Ritu Khare
- National Center for Biotechnology Information, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA
| | - Yuqing Mao
- National Center for Biotechnology Information, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA
| | - Robert Leaman
- National Center for Biotechnology Information, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA
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Wei CH, Leaman R, Lu Z. SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedicine. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2014; 2014:138-146. [PMID: 25844401 PMCID: PMC4384177 DOI: 10.1145/2649387.2649420] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Many text-mining studies have focused on the issue of named entity recognition and normalization, especially in the field of biomedical natural language processing. However, entity recognition is a complicated and difficult task in biomedical text. One particular challenge is to identify and resolve composite named entities, where a single span refers to more than one concept(e.g., BRCA1/2). Most bioconcept recognition and normalization studies have either ignored this issue, used simple ad-hoc rules, or only handled coordination ellipsis, which is only one of the many types of composite mentions studied in this work. No systematic methods for simplifying composite mentions have been previously reported, making a robust approach greatly needed. To this end, we propose a hybrid approach by integrating a machine learning model with a pattern identification strategy to identify the antecedent and conjuncts regions of a concept mention, and then reassemble the composite mention using those identified regions. Our method, which we have named SimConcept, is the first method to systematically handle most types of composite mentions. Our method achieves high performance in identifying and resolving composite mentions for three fundamental biological entities: genes (89.29% in F-measure), diseases (85.52% in F-measure) and chemicals (84.04% in F-measure). Furthermore, our results show that, using our SimConcept method can subsequently help improve the performance of gene and disease concept recognition and normalization.
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Affiliation(s)
- Chih-Hsuan Wei
- 8600 Rockville Pike, National Center for Biotechnology Information (NCBI), Bethesda, Maryland, USA, 20894
| | - Robert Leaman
- 8600 Rockville Pike, National Center for Biotechnology Information (NCBI), Bethesda, Maryland, USA, 20894
| | - Zhiyong Lu
- 8600 Rockville Pike, National Center for Biotechnology Information (NCBI), Bethesda, Maryland, USA, 20894
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