1
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Neves D, Duarte-Pereira S, Matos S, Silva RM. Proteostasis networks in aging: novel insights from text-mining approaches. Biogerontology 2023:10.1007/s10522-023-10027-0. [PMID: 37004691 PMCID: PMC10267007 DOI: 10.1007/s10522-023-10027-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/06/2023] [Indexed: 04/04/2023]
Abstract
Aging is a topic of paramount importance in an increasingly elderly society and has been the focus of extensive research. Protein homeostasis (proteostasis) decline is a hallmark in aging and several age-related diseases, but which specific proteins and mechanisms are involved in proteostasis (de)regulation during the aging process remain largely unknown. Here, we used different text-mining tools complemented with protein-protein interaction data to address this complex topic. Analysis of the integrated protein interaction networks identified novel proteins and pathways associated to proteostasis mechanisms and aging or age-related disorders, indicating that this approach is useful to identify previously unknown links and for retrieving information of potential novel biomarkers or therapeutic targets.
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Affiliation(s)
- Diogo Neves
- Department of Medical Sciences & iBiMED, University of Aveiro, Aveiro, Portugal
| | - Sara Duarte-Pereira
- Department of Medical Sciences & iBiMED, University of Aveiro, Aveiro, Portugal
- IEETA, University of Aveiro, Aveiro, Portugal
| | - Sérgio Matos
- IEETA, University of Aveiro, Aveiro, Portugal
- DETI, University of Aveiro, Aveiro, Portugal
| | - Raquel M Silva
- Department of Medical Sciences & iBiMED, University of Aveiro, Aveiro, Portugal.
- Universidade Católica Portuguesa, Faculdade de Medicina Dentária, Centro de Investigação Interdisciplinar em Saúde, Estrada da Circunvalação, 3504-505, Viseu, Portugal.
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2
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Giachelle F, Irrera O, Silvello G. MedTAG: a portable and customizable annotation tool for biomedical documents. BMC Med Inform Decis Mak 2021; 21:352. [PMID: 34922517 PMCID: PMC8684237 DOI: 10.1186/s12911-021-01706-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 12/01/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Semantic annotators and Natural Language Processing (NLP) methods for Named Entity Recognition and Linking (NER+L) require plenty of training and test data, especially in the biomedical domain. Despite the abundance of unstructured biomedical data, the lack of richly annotated biomedical datasets poses hindrances to the further development of NER+L algorithms for any effective secondary use. In addition, manual annotation of biomedical documents performed by physicians and experts is a costly and time-consuming task. To support, organize and speed up the annotation process, we introduce MedTAG, a collaborative biomedical annotation tool that is open-source, platform-independent, and free to use/distribute. RESULTS We present the main features of MedTAG and how it has been employed in the histopathology domain by physicians and experts to annotate more than seven thousand clinical reports manually. We compare MedTAG with a set of well-established biomedical annotation tools, including BioQRator, ezTag, MyMiner, and tagtog, comparing their pros and cons with those of MedTag. We highlight that MedTAG is one of the very few open-source tools provided with an open license and a straightforward installation procedure supporting cross-platform use. CONCLUSIONS MedTAG has been designed according to five requirements (i.e. available, distributable, installable, workable and schematic) defined in a recent extensive review of manual annotation tools. Moreover, MedTAG satisfies 20 over 22 criteria specified in the same study.
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Affiliation(s)
- Fabio Giachelle
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Ornella Irrera
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padua, Padua, Italy
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3
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Neves M, Ševa J. An extensive review of tools for manual annotation of documents. Brief Bioinform 2021; 22:146-163. [PMID: 31838514 PMCID: PMC7820865 DOI: 10.1093/bib/bbz130] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Annotation tools are applied to build training and test corpora, which are essential for the development and evaluation of new natural language processing algorithms. Further, annotation tools are also used to extract new information for a particular use case. However, owing to the high number of existing annotation tools, finding the one that best fits particular needs is a demanding task that requires searching the scientific literature followed by installing and trying various tools. METHODS We searched for annotation tools and selected a subset of them according to five requirements with which they should comply, such as being Web-based or supporting the definition of a schema. We installed the selected tools (when necessary), carried out hands-on experiments and evaluated them using 26 criteria that covered functional and technical aspects. We defined each criterion on three levels of matches and a score for the final evaluation of the tools. RESULTS We evaluated 78 tools and selected the following 15 for a detailed evaluation: BioQRator, brat, Catma, Djangology, ezTag, FLAT, LightTag, MAT, MyMiner, PDFAnno, prodigy, tagtog, TextAE, WAT-SL and WebAnno. Full compliance with our 26 criteria ranged from only 9 up to 20 criteria, which demonstrated that some tools are comprehensive and mature enough to be used on most annotation projects. The highest score of 0.81 was obtained by WebAnno (of a maximum value of 1.0).
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Affiliation(s)
- Mariana Neves
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Jurica Ševa
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
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4
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Islamaj R, Kwon D, Kim S, Lu Z. TeamTat: a collaborative text annotation tool. Nucleic Acids Res 2020; 48:W5-W11. [PMID: 32383756 DOI: 10.1093/nar/gkaa333] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/16/2020] [Accepted: 04/22/2020] [Indexed: 12/20/2022] Open
Abstract
Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.
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Affiliation(s)
- Rezarta Islamaj
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Dongseop Kwon
- School of Software Convergence, Myongji University, Seoul 03674, South Korea
| | - Sun Kim
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA
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5
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Kwon D, Kim S, Wei CH, Leaman R, Lu Z. ezTag: tagging biomedical concepts via interactive learning. Nucleic Acids Res 2019; 46:W523-W529. [PMID: 29788413 PMCID: PMC6030907 DOI: 10.1093/nar/gky428] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 05/07/2018] [Indexed: 12/22/2022] Open
Abstract
Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.
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Affiliation(s)
- Dongseop Kwon
- School of Software Convergence, Myongji University, Seoul 03674, South Korea
| | - Sun Kim
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Robert Leaman
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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6
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Li M, He Q, Ma J, He F, Zhu Y, Chang C, Chen T. PPICurator: A Tool for Extracting Comprehensive Protein-Protein Interaction Information. Proteomics 2019; 19:e1800291. [PMID: 30521143 DOI: 10.1002/pmic.201800291] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 11/12/2018] [Indexed: 11/07/2022]
Abstract
Protein-protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web-based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state-of-the-art tools. Moreover, it outperforms state-of-the-art tools in the accuracy of PPI relation extraction measured by F-score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn.
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Affiliation(s)
- Mansheng Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
| | - Qiang He
- School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria, 3122, Australia
| | - Jie Ma
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
| | - Tao Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, 102206, P. R. China
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7
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Bada M, Vasilevsky N, Baumgartner WA, Haendel M, Hunter LE. Gold-standard ontology-based anatomical annotation in the CRAFT Corpus. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:4780291. [PMID: 31725864 PMCID: PMC7243923 DOI: 10.1093/database/bax087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
Gold-standard annotated corpora have become important resources for the training and testing of natural-language-processing (NLP) systems designed to support biocuration efforts, and ontologies are increasingly used to facilitate curational consistency and semantic integration across disparate resources. Bringing together the respective power of these, the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of full-length, open-access biomedical journal articles with extensive manually created syntactic, formatting and semantic markup, was previously created and released. This initial public release has already been used in multiple projects to drive development of systems focused on a variety of biocuration, search, visualization, and semantic and syntactic NLP tasks. Building on its demonstrated utility, we have expanded the CRAFT Corpus with a large set of manually created semantic annotations relying on Uberon, an ontology representing anatomical entities and life-cycle stages of multicellular organisms across species as well as types of multicellular organisms defined in terms of life-cycle stage and sexual characteristics. This newly created set of annotations, which has been added for v2.1 of the corpus, is by far the largest publicly available collection of gold-standard anatomical markup and is the first large-scale effort at manual markup of biomedical text relying on the entirety of an anatomical terminology, as opposed to annotation with a small number of high-level anatomical categories, as performed in previous corpora. In addition to presenting and discussing this newly available resource, we apply it to provide a performance baseline for the automatic annotation of anatomical concepts in biomedical text using a prominent concept recognition system. The full corpus, released with a CC BY 3.0 license, may be downloaded from http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml. Database URL: http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml
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Affiliation(s)
- Michael Bada
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
| | - Nicole Vasilevsky
- Ontology Development Group, Library, Oregon Health & Science University, 318 SW Sam Jackson, Park Road, Portland, OR 97239, USA
| | - William A Baumgartner
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
| | - Melissa Haendel
- Ontology Development Group, Library, Oregon Health & Science University, 318 SW Sam Jackson, Park Road, Portland, OR 97239, USA
| | - Lawrence E Hunter
- School of Medicine, Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., P.O. Box 6511, MS 8303, Aurora, CO 80045-0511, USA
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8
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Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer. BIOMED RESEARCH INTERNATIONAL 2017; 2017:8327980. [PMID: 29214177 PMCID: PMC5682045 DOI: 10.1155/2017/8327980] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/11/2017] [Accepted: 10/02/2017] [Indexed: 12/28/2022]
Abstract
Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.
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9
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SCALEUS: Semantic Web Services Integration for Biomedical Applications. J Med Syst 2017; 41:54. [PMID: 28214993 DOI: 10.1007/s10916-017-0705-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 02/09/2017] [Indexed: 10/20/2022]
Abstract
In recent years, we have witnessed an explosion of biological data resulting largely from the demands of life science research. The vast majority of these data are freely available via diverse bioinformatics platforms, including relational databases and conventional keyword search applications. This type of approach has achieved great results in the last few years, but proved to be unfeasible when information needs to be combined or shared among different and scattered sources. During recent years, many of these data distribution challenges have been solved with the adoption of semantic web. Despite the evident benefits of this technology, its adoption introduced new challenges related with the migration process, from existent systems to the semantic level. To facilitate this transition, we have developed Scaleus, a semantic web migration tool that can be deployed on top of traditional systems in order to bring knowledge, inference rules, and query federation to the existent data. Targeted at the biomedical domain, this web-based platform offers, in a single package, straightforward data integration and semantic web services that help developers and researchers in the creation process of new semantically enhanced information systems. SCALEUS is available as open source at http://bioinformatics-ua.github.io/scaleus/ .
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10
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Sernadela P, Oliveira JL. A semantic-based workflow for biomedical literature annotation. Database (Oxford) 2017; 2017:4635750. [PMID: 29220478 PMCID: PMC5691355 DOI: 10.1093/database/bax088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 10/02/2017] [Accepted: 10/30/2017] [Indexed: 11/12/2022]
Abstract
Computational annotation of textual information has taken on an important role in knowledge extraction from the biomedical literature, since most of the relevant information from scientific findings is still maintained in text format. In this endeavour, annotation tools can assist in the identification of biomedical concepts and their relationships, providing faster reading and curation processes, with reduced costs. However, the separate usage of distinct annotation systems results in highly heterogeneous data, as it is difficult to efficiently combine and exchange this valuable asset. Moreover, despite the existence of several annotation formats, there is no unified way to integrate miscellaneous annotation outcomes into a reusable, sharable and searchable structure. Taking up this challenge, we present a modular architecture for textual information integration using semantic web features and services. The solution described allows the migration of curation data into a common model, providing a suitable transition process in which multiple annotation data can be integrated and enriched, with the possibility of being shared, compared and reused across semantic knowledge bases.
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Affiliation(s)
- Pedro Sernadela
- University of Aveiro, DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - José Luís Oliveira
- University of Aveiro, DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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11
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Wang Q, S Abdul S, Almeida L, Ananiadou S, Balderas-Martínez YI, Batista-Navarro R, Campos D, Chilton L, Chou HJ, Contreras G, Cooper L, Dai HJ, Ferrell B, Fluck J, Gama-Castro S, George N, Gkoutos G, Irin AK, Jensen LJ, Jimenez S, Jue TR, Keseler I, Madan S, Matos S, McQuilton P, Milacic M, Mort M, Natarajan J, Pafilis E, Pereira E, Rao S, Rinaldi F, Rothfels K, Salgado D, Silva RM, Singh O, Stefancsik R, Su CH, Subramani S, Tadepally HD, Tsaprouni L, Vasilevsky N, Wang X, Chatr-Aryamontri A, Laulederkind SJF, Matis-Mitchell S, McEntyre J, Orchard S, Pundir S, Rodriguez-Esteban R, Van Auken K, Lu Z, Schaeffer M, Wu CH, Hirschman L, Arighi CN. Overview of the interactive task in BioCreative V. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw119. [PMID: 27589961 PMCID: PMC5009325 DOI: 10.1093/database/baw119] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 07/28/2016] [Indexed: 11/14/2022]
Abstract
Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se. In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested. Database URL:http://www.biocreative.org
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Affiliation(s)
- Qinghua Wang
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA
| | - Shabbir S Abdul
- International Centre of Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Lara Almeida
- DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - Sophia Ananiadou
- National Centre for Text Mining, University of Manchester, Manchester, UK
| | | | | | | | - Lucy Chilton
- Northern Institute for Cancer Research, Newcastle University, New Castle, UK
| | - Hui-Jou Chou
- Rutgers University-Camden, Camden, NJ 08102, USA
| | - Gabriela Contreras
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, 04510 Ciudad de México, México
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University Corvallis, OR 97331, USA
| | - Hong-Jie Dai
- Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan
| | - Barbra Ferrell
- College of Agriculture and Natural Resources, University of Delaware, Newark, DE 19711, USA
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 St. Augustin, Germany
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, 04510 Ciudad de México, México
| | | | - Georgios Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TT, UK
| | - Afroza K Irin
- Life Science Informatics, University of Bonn, Bonn, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Silvia Jimenez
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland
| | - Toni R Jue
- Prince of Wales Clinical School, University of New South Wales NSW, Sydney, New South Wales, Australia
| | | | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 St. Augustin, Germany
| | - Sérgio Matos
- DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | | | - Marija Milacic
- Department of Informatics and Bio-Computing, Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Matthew Mort
- HGMD, Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, UK
| | - Jeyakumar Natarajan
- Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Emiliano Pereira
- Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Shruti Rao
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC 20007, USA
| | - Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Karen Rothfels
- Department of Informatics and Bio-Computing, Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - David Salgado
- GMGF, Aix-Marseille Universite, 13385 Marseille, France Inserm, UMR_S 910, 13385 Marseille, France
| | - Raquel M Silva
- Department of Medical Sciences, iBiMED & IEETA, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Onkar Singh
- Taipei Medical University Graduate Institute of Biomedical informatics, Taipei, Taiwan
| | | | - Chu-Hsien Su
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Suresh Subramani
- Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India
| | | | - Loukia Tsaprouni
- Institute of Sport and Physical Activity Research (ISPAR), University of Bedfordshire, Bedford, UK
| | - Nicole Vasilevsky
- Ontology Development Group, Oregon Health & Science University, Portland, OR 97239, USA
| | - Xiaodong Wang
- WormBase Consortium, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | | | | | | | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Sangya Pundir
- European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | - Kimberly Van Auken
- WormBase Consortium, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Institutes of Health, Bethesda, MD 20894, USA
| | - Mary Schaeffer
- MaizeGDB USDA ARS and University of Missouri, Columbia, MO 65211, USA
| | - Cathy H Wu
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA
| | | | - Cecilia N Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA
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Shin SY, Kim S, Wilbur WJ, Kwon D. BioC viewer: a web-based tool for displaying and merging annotations in BioC. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw106. [PMID: 27515823 PMCID: PMC4980568 DOI: 10.1093/database/baw106] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 06/23/2016] [Indexed: 12/20/2022]
Abstract
BioC is an XML-based format designed to provide interoperability for text mining tools and manual curation results. A challenge of BioC as a standard format is to align annotations from multiple systems. Ideally, this should not be a major problem if users follow guidelines given by BioC key files. Nevertheless, the misalignment between text and annotations happens quite often because different systems tend to use different software development environments, e.g. ASCII vs. Unicode. We first implemented the BioC Viewer to assist BioGRID curators as a part of the BioCreative V BioC track (Collaborative Biocurator Assistant Task). For the BioC track, the BioC Viewer helped curate protein-protein interaction and genetic interaction pairs appearing in full-text articles. Here, we describe the BioC Viewer itself as well as improvements made to the BioC Viewer since the BioCreative V Workshop to address the misalignment issue of BioC annotations. While uploading BioC files, a BioC merge process is offered when there are files from the same full-text article. If there is a mismatch between an annotated offset and text, the BioC Viewer adjusts the offset to correctly align with the text. The BioC Viewer has a user-friendly interface, where most operations can be performed within a few mouse clicks. The feedback from BioGRID curators has been positive for the web interface, particularly for its usability and learnability. Database URL: http://viewer.bioqrator.org
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Affiliation(s)
- Soo-Yong Shin
- Department of Biomedical Informatics, Asan Medical Center, Seoul 05505, Korea
| | - Sun Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, MD 20894, USA
| | - W John Wilbur
- National Center for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, MD 20894, USA
| | - Dongseop Kwon
- Deptartment of Computer Engineering, Myongji University, Yongin, Gyeonggi-do 17058, Korea
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Matos S, Campos D, Pinho R, Silva RM, Mort M, Cooper DN, Oliveira JL. Mining clinical attributes of genomic variants through assisted literature curation in Egas. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw096. [PMID: 27278817 PMCID: PMC4897594 DOI: 10.1093/database/baw096] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 05/15/2016] [Indexed: 01/08/2023]
Abstract
The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/. Database URL: https://demo.bmd-software.com/egas/
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Affiliation(s)
- Sérgio Matos
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193, Portugal
| | | | - Renato Pinho
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193, Portugal
| | - Raquel M Silva
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193, Portugal Department of Medical Sciences, iBiMED, University of Aveiro, Aveiro, 3810-193, Portugal
| | | | - David N Cooper
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, UK
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Pafilis E, Buttigieg PL, Ferrell B, Pereira E, Schnetzer J, Arvanitidis C, Jensen LJ. EXTRACT: interactive extraction of environment metadata and term suggestion for metagenomic sample annotation. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw005. [PMID: 26896844 PMCID: PMC4761108 DOI: 10.1093/database/baw005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 01/11/2016] [Indexed: 12/11/2022]
Abstract
The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, well documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15-25% and helps curators to detect terms that would otherwise have been missed. Database URL: https://extract.hcmr.gr/.
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Affiliation(s)
- Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O. Box 2214, 71003 Heraklion, Crete, Greece,
| | - Pier Luigi Buttigieg
- Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, D-27570 Bremerhaven, Germany
| | - Barbra Ferrell
- Delaware Biotechnology Institute, Newark, DE 19711, Delaware, USA
| | - Emiliano Pereira
- Max Planck Institute for Marine Microbiology, Celsiusstr. 1, 28359, Bremen, Germany
| | - Julia Schnetzer
- Max Planck Institute for Marine Microbiology, Celsiusstr. 1, 28359, Bremen, Germany, Jacobs University gGmbH, School of Engineering and Sciences, Campus Ring 1, 28759, Bremen, Germany, and
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O. Box 2214, 71003 Heraklion, Crete, Greece
| | - Lars Juhl Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen, Denmark
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