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Chen J, Goudey B, Geard N, Verspoor K. Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation. Bioinformatics 2024; 40:i390-i400. [PMID: 38940182 PMCID: PMC11256942 DOI: 10.1093/bioinformatics/btae246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. RESULTS We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies. This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. AVAILABILITY AND IMPLEMENTATION https://github.com/jiyuc/de-inconsistency.
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Affiliation(s)
- Jiyu Chen
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
- Data61, The Commonwealth Scientific and Industrial Research Organisation, Marsfield 2122, NSW, Australia
| | - Benjamin Goudey
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Victoria 3000, Australia
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Feinberg JI, Schrott R, Ladd-Acosta C, Newschaffer CJ, Hertz-Picciotto I, Croen LA, Daniele Fallin M, Feinberg AP, Volk HE. Epigenetic changes in sperm are associated with paternal and child quantitative autistic traits in an autism-enriched cohort. Mol Psychiatry 2024; 29:43-53. [PMID: 37100868 DOI: 10.1038/s41380-023-02046-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/28/2023]
Abstract
There is a need to consider paternal contributions to autism spectrum disorder (ASD) more strongly. Autism etiology is complex, and heritability is not explained by genetics alone. Understanding paternal gametic epigenetic contributions to autism could help fill this knowledge gap. In the present study, we explored whether paternal autistic traits, and the sperm epigenome, were associated with autistic traits in children at 36 months enrolled in the Early Autism Risk Longitudinal Investigation (EARLI) cohort. EARLI is a pregnancy cohort that recruited and enrolled pregnant women in the first half of pregnancy who already had a child with ASD. After maternal enrollment, EARLI fathers were approached and asked to provide a semen specimen. Participants were included in the present study if they had genotyping, sperm methylation data, and Social Responsiveness Scale (SRS) score data available. Using the CHARM array, we performed genome-scale methylation analyses on DNA from semen samples contributed by EARLI fathers. The SRS-a 65-item questionnaire measuring social communication deficits on a quantitative scale-was used to evaluate autistic traits in EARLI fathers (n = 45) and children (n = 31). We identified 94 significant child SRS-associated differentially methylated regions (DMRs), and 14 significant paternal SRS-associated DMRs (fwer p < 0.05). Many child SRS-associated DMRs were annotated to genes implicated in ASD and neurodevelopment. Six DMRs overlapped across the two outcomes (fwer p < 0.1), and, 16 DMRs overlapped with previous child autistic trait findings at 12 months of age (fwer p < 0.05). Child SRS-associated DMRs contained CpG sites independently found to be differentially methylated in postmortem brains of individuals with and without autism. These findings suggest paternal germline methylation is associated with autistic traits in 3-year-old offspring. These prospective results for autism-associated traits, in a cohort with a family history of ASD, highlight the potential importance of sperm epigenetic mechanisms in autism.
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Affiliation(s)
- Jason I Feinberg
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rose Schrott
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Craig J Newschaffer
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, State College, PA, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, MIND (Medical Investigations of Neurodevelopmental Disorders) Institute, University of California, Davis, CA, USA
| | - Lisa A Croen
- Autism Research Program, Division of Research, Kaiser Permanente, Oakland, CA, USA
| | - M Daniele Fallin
- Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Andrew P Feinberg
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Center for Epigenetics, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Heather E Volk
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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3
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Chen J, Geard N, Zobel J, Verspoor K. Automatic consistency assurance for literature-based gene ontology annotation. BMC Bioinformatics 2021; 22:565. [PMID: 34823464 PMCID: PMC8620237 DOI: 10.1186/s12859-021-04479-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. RESULTS In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. CONCLUSIONS Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.
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Affiliation(s)
- Jiyu Chen
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Justin Zobel
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia
| | - Karin Verspoor
- School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia. .,School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia.
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Stojanov R, Popovski G, Cenikj G, Koroušić Seljak B, Eftimov T. A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation. J Med Internet Res 2021; 23:e28229. [PMID: 34383671 PMCID: PMC8415558 DOI: 10.2196/28229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/13/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources. OBJECTIVE In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction. METHODS We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags. RESULTS All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%. CONCLUSIONS FoodNER can be used to extract and annotate food entities in 5 different tasks: food versus nonfood entities and distinguishing food entities on the level of food groups by using the closest Hansard semantic tags, the parent Hansard semantic tags, the FoodOn semantic tags, or the Systematized Nomenclature of Medicine Clinical Terms semantic tags.
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Affiliation(s)
- Riste Stojanov
- Faculty of Computer Science and Engineering, Ss Cyril and Methodius, University- Skopje, Skopje, the Former Yugoslav Republic of Macedonia
| | - Gorjan Popovski
- Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
| | - Gjorgjina Cenikj
- Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
| | | | - Tome Eftimov
- Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia
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Hobbs ET, Goralski SM, Mitchell A, Simpson A, Leka D, Kotey E, Sekira M, Munro JB, Nadendla S, Jackson R, Gonzalez-Aguirre A, Krallinger M, Giglio M, Erill I. ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts. Front Res Metr Anal 2021; 6:674205. [PMID: 34327299 PMCID: PMC8313968 DOI: 10.3389/frma.2021.674205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/28/2021] [Indexed: 11/20/2022] Open
Abstract
Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.
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Affiliation(s)
- Elizabeth T Hobbs
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Stephen M Goralski
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Ashley Mitchell
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Andrew Simpson
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Dorjan Leka
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Emmanuel Kotey
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Matt Sekira
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - James B Munro
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Rebecca Jackson
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Martin Krallinger
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Ivan Erill
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
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6
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Huang MS, Lai PT, Lin PY, You YT, Tsai RTH, Hsu WL. Biomedical named entity recognition and linking datasets: survey and our recent development. Brief Bioinform 2020; 21:2219-2238. [DOI: 10.1093/bib/bbaa054] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/29/2020] [Accepted: 03/31/2020] [Indexed: 11/14/2022] Open
Abstract
AbstractNatural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein–protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein–protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Ming-Siang Huang
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Po-Ting Lai
- Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan
| | - Pei-Yen Lin
- Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan
| | - Yu-Ting You
- Intelligent Agent Systems Laboratory, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Richard Tzong-Han Tsai
- Intelligent Information Service Research Laboratory, Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Wen-Lian Hsu
- Intelligent Agent Systems Laboratory, Institute of Information Science, Academia Sinica, Taipei, Taiwan
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Farahmand S, Riley T, Zarringhalam K. ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction. J Biomed Inform 2019; 102:103353. [PMID: 31857203 DOI: 10.1016/j.jbi.2019.103353] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 11/22/2019] [Accepted: 12/10/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. METHODS In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. RESULTS Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.
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Affiliation(s)
- Saman Farahmand
- Computational Sciences PhD program, University of Massachusetts Boston, Boston, USA; Department of Biology, University of Massachusetts Boston, Boston, USA
| | - Todd Riley
- Department of Biology, University of Massachusetts Boston, Boston, USA
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8
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Wu J, Yin Q, Zhang C, Geng J, Wu H, Hu H, Ke X, Zhang Y. Function Prediction for G Protein-Coupled Receptors through Text Mining and Induction Matrix Completion. ACS OMEGA 2019; 4:3045-3054. [PMID: 31459527 PMCID: PMC6649004 DOI: 10.1021/acsomega.8b02454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/11/2019] [Indexed: 06/10/2023]
Abstract
G protein-coupled receptors (GPCRs) constitute the key component of cellular signal transduction. Accurately annotating the biological functions of GPCR proteins is vital to the understanding of the physiological processes they involve in. With the rapid development of text mining technologies and the exponential growth of biomedical literature, it becomes urgent to explore biological functional information from various literature for systematically and reliably annotating these known GPCRs. We design a novel three-stage approach, TM-IMC, using text mining and inductive matrix completion, for automated prediction of the gene ontology (GO) terms of the GPCR proteins. Large-scale benchmark tests show that inductive matrix completion models contribute to GPCR-GO association prediction for both molecular function and biological process aspects. Moreover, our detailed data analysis shows that information extracted from GPCR-associated literature indeed contributes to the prediction of GPCR-GO associations. The study demonstrated a new avenue to enhance the accuracy of GPCR function annotation through the combination of text mining and induction matrix completion over baseline methods in critical assessment of protein function annotation algorithms and literature-based GO annotation methods. Source codes of TM-IMC and the involved datasets can be freely downloaded from https://zhanglab.ccmb.med.umich.edu/TM-IMC for academic purposes.
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Affiliation(s)
- Jiansheng Wu
- School
of Geographic and Biological Information and School of Telecommunication and
Information Engineering, Nanjing University
of Posts and Telecommunications, Nanjing 210023, China
| | - Qin Yin
- School
of Geographic and Biological Information and School of Telecommunication and
Information Engineering, Nanjing University
of Posts and Telecommunications, Nanjing 210023, China
| | - Chengxin Zhang
- Department of Computational Medicine
and Bioinformatics and Department of Biological
Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Jingjing Geng
- School
of Geographic and Biological Information and School of Telecommunication and
Information Engineering, Nanjing University
of Posts and Telecommunications, Nanjing 210023, China
| | - Hongjie Wu
- School
of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Haifeng Hu
- School
of Geographic and Biological Information and School of Telecommunication and
Information Engineering, Nanjing University
of Posts and Telecommunications, Nanjing 210023, China
| | - Xiaoyan Ke
- Child
Mental Health Research Center, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Yang Zhang
- Department of Computational Medicine
and Bioinformatics and Department of Biological
Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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9
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Ding R, Qu Y, Wu CH, Vijay-Shanker K. Automatic gene annotation using GO terms from cellular component domain. BMC Med Inform Decis Mak 2018; 18:119. [PMID: 30526566 PMCID: PMC6284271 DOI: 10.1186/s12911-018-0694-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. This process is very time-consuming and labor-intensive. As a result, many MODs can afford to curate only a fraction of relevant articles. Methods GO terms from the CC domain can be essentially divided into two sub-hierarchies: subcellular location terms, and protein complex terms. We cast the task of gene annotation using GO terms from the CC domain as relation extraction between gene and other entities: (1) extract cases where a protein is found to be in a subcellular location, and (2) extract cases where a protein is a subunit of a protein complex. For each relation extraction task, we use an approach based on triggers and syntactic dependencies to extract the desired relations among entities. Results We tested our approach on the BC4GO test set, a publicly available corpus for GO annotation. Our approach obtains a F1-score of 71%, a precision of 91% and a recall of 58% for predicting GO terms from CC Domain for given genes. Conclusions We have described a novel approach of treating gene annotation with GO terms from CC domain as two relation extraction subtasks. Evaluation results show that our approach achieves a F1-score of 71% for predicting GO terms for given genes. Thereby our approach can be used to accelerate the process of GO annotation for the bio-annotators.
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Affiliation(s)
- Ruoyao Ding
- School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China
| | - Yingying Qu
- School of Business, Guangdong University of Foreign Studies, Guangzhou, China.
| | - Cathy H Wu
- Department of Computer and Information Science, University of Delaware, Newark, DE, 19716, USA
| | - K Vijay-Shanker
- Department of Computer and Information Science, University of Delaware, Newark, DE, 19716, USA
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10
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Cejuela JM, Vinchurkar S, Goldberg T, Prabhu Shankar MS, Baghudana A, Bojchevski A, Uhlig C, Ofner A, Raharja-Liu P, Jensen LJ, Rost B. LocText: relation extraction of protein localizations to assist database curation. BMC Bioinformatics 2018; 19:15. [PMID: 29343218 PMCID: PMC5773052 DOI: 10.1186/s12859-018-2021-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 01/10/2018] [Indexed: 12/31/2022] Open
Abstract
Background The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence. Results LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot. Conclusions LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot. Electronic supplementary material The online version of this article (doi:10.1186/s12859-018-2021-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Juan Miguel Cejuela
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.
| | - Shrikant Vinchurkar
- Microsoft, Microsoft Development Center Copenhagen, Kanalvej 7, Kongens Lyngby, 2800, Denmark
| | - Tatyana Goldberg
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - Madhukar Sollepura Prabhu Shankar
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - Ashish Baghudana
- Department of Computer Science and Information Systems, Birla Institute of Technology and Science K. K. Birla Goa Campus, Zuarinagar, 403726, Goa, India
| | - Aleksandar Bojchevski
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - Carsten Uhlig
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - André Ofner
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - Pandu Raharja-Liu
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, 2200, Denmark.
| | - Burkhard Rost
- Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany. .,Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching/Munich, 85748, Germany. .,TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany. .,Columbia University, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, USA. .,New York Consortium on Membrane Protein Structure (NYCOMPS), 701 West, 168th Street, New York, 10032, NY, USA.
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11
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Yang CJ, Chiang JH. Gene ontology concept recognition using named concept: understanding the various presentations of the gene functions in biomedical literature. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:5146423. [PMID: 30376045 PMCID: PMC6204799 DOI: 10.1093/database/bay115] [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: 02/21/2018] [Accepted: 09/29/2018] [Indexed: 11/14/2022]
Abstract
OBJECTIVE A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedical literature. Gene ontology concept recognition (GOCR) is a biomedical natural language processing task used to extract and normalize the mentions of gene ontology (GO), the controlled vocabulary for gene functions across many species, from biomedical text. The previous GOCR systems, using either rule-based or machine-learning methods, treated GO concepts as separate terms and did not have an efficient way of sharing the common synonyms among the concepts. MATERIALS AND METHODS We used the CRAFT corpus in this study. Targeting the compositional structure of the GO, we introduced named concept, the basic conceptual unit which has a conserved name and is used in other complex concepts. Using the named concepts, we separated the GOCR task into dictionary-matching and machine-learning steps. By harvesting the surface names used in the training data, we wildly boosted the synonyms of GO concepts via the connection of the named concepts and then enhanced the capability to recognize more GO concepts in the text. The source code is available athttps://github.com/jeroyang/ncgocr. RESULTS Named concept gene ontology concept recognizer (NCGOCR) achieved 0.804 precision and 0.715 recall by correct recognition of the non-standard mentions of the GO concepts. DISCUSSION The lack of consensus on GO naming causes diversity in the GO mentions in biomedical manuscripts. The high performance is owed to the stability of the composing GO concepts and the lack of variance in the spelling of named concepts. CONCLUSION NCGOCR reduced the arduous work of GO annotation and amended the process of searching for the biomarkers or drug targets, leading to improved biomarker development and greater success in precision medicine.
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Affiliation(s)
- Chia-Jung Yang
- Department of Computer Science and Information Engineering, National Cheng Kung University, 1, University Road, Tainan City, Taiwan.,Department of Radiology, Taitung Mackay Memorial Hospital, 1, Lane 303, Changsha Street, Taitung City, Taiwan
| | - Jung-Hsien Chiang
- Department of Computer Science and Information Engineering, National Cheng Kung University, 1, University Road, Tainan City, Taiwan
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12
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Britan A, Cusin I, Hinard V, Mottin L, Pasche E, Gobeill J, Rech de Laval V, Gleizes A, Teixeira D, Michel PA, Ruch P, Gaudet P. Accelerating annotation of articles via automated approaches: evaluation of the neXtA5 curation-support tool by neXtProt. Database (Oxford) 2018; 2018:5255187. [PMID: 30576492 PMCID: PMC6301339 DOI: 10.1093/database/bay129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 10/04/2018] [Accepted: 11/09/2018] [Indexed: 11/14/2022]
Abstract
The development of efficient text-mining tools promises to boost the curation workflow by significantly reducing the time needed to process the literature into biological databases. We have developed a curation support tool, neXtA5, that provides a search engine coupled with an annotation system directly integrated into a biocuration workflow. neXtA5 assists curation with modules optimized for the thevarious curation tasks: document triage, entity recognition and information extraction.Here, we describe the evaluation of neXtA5 by expert curators. We first assessed the annotations of two independent curators to provide a baseline for comparison. To evaluate the performance of neXtA5, we submitted requests and compared the neXtA5 results with the manual curation. The analysis focuses on the usability of neXtA5 to support the curation of two types of data: biological processes (BPs) and diseases (Ds). We evaluated the relevance of the papers proposed as well as the recall and precision of the suggested annotations.The evaluation of document triage by neXtA5 precision showed that both curators agree with neXtA5 for 67 (BP) and 63% (D) of abstracts, while curators agree on accepting or rejecting an abstract ~80% of the time. Hence, the precision of the triage system is satisfactory.For concept extraction, curators approved 35 (BP) and 25% (D) of the neXtA5 annotations. Conversely, neXtA5 successfully annotated up to 36 (BP) and 68% (D) of the terms identified by curators. The user feedback obtained in these tests highlighted the need for improvement in the ranking function of neXtA5 annotations. Therefore, we transformed the information extraction component into an annotation ranking system. This improvement results in a top precision (precision at first rank) of 59 (D) and 63% (BP). These results suggest that when considering only the first extracted entity, the current system achieves a precision comparable with expert biocurators.
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Affiliation(s)
- Aurore Britan
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Isabelle Cusin
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Valérie Hinard
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Luc Mottin
- Haute école spécialisée de Suisse occidentale, Haute Ecole de Gestion de Genève, Carouge, Switzerland
- SIB Text Mining, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Emilie Pasche
- Haute école spécialisée de Suisse occidentale, Haute Ecole de Gestion de Genève, Carouge, Switzerland
- SIB Text Mining, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Julien Gobeill
- Haute école spécialisée de Suisse occidentale, Haute Ecole de Gestion de Genève, Carouge, Switzerland
- SIB Text Mining, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Valentine Rech de Laval
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Anne Gleizes
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Daniel Teixeira
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Pierre-André Michel
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Patrick Ruch
- Haute école spécialisée de Suisse occidentale, Haute Ecole de Gestion de Genève, Carouge, Switzerland
- SIB Text Mining, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
| | - Pascale Gaudet
- Computer and Laboratory Investigation of Proteins of Human Origin Group, SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland
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13
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Ravikumar KE, Rastegar-Mojarad M, Liu H. BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3053439. [PMID: 28365720 PMCID: PMC5467463 DOI: 10.1093/database/baw156] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 11/07/2016] [Indexed: 12/22/2022]
Abstract
Extracting meaningful relationships with semantic significance from biomedical literature is often a challenging task. BioCreative V track4 challenge for the first time has organized a comprehensive shared task to test the robustness of the text-mining algorithms in extracting semantically meaningful assertions from the evidence statement in biomedical text. In this work, we tested the ability of a rule-based semantic parser to extract Biological Expression Language (BEL) statements from evidence sentences culled out of biomedical literature as part of BioCreative V Track4 challenge. The system achieved an overall best F-measure of 21.29% in extracting the complete BEL statement. For relation extraction, the system achieved an F-measure of 65.13% on test data set. Our system achieved the best performance in five of the six criteria that was adopted for evaluation by the task organizers. Lack of ability to derive semantic inferences, limitation in the rule sets to map the textual extractions to BEL function were some of the reasons for low performance in extracting the complete BEL statement. Post shared task we also evaluated the impact of differential NER components on the ability to extract BEL statements on the test data sets besides making a single change in the rule sets that translate relation extractions into a BEL statement. There is a marked improvement by over 20% in the overall performance of the BELMiner’s capability to extract BEL statement on the test set. The system is available as a REST-API at http://54.146.11.205:8484/BELXtractor/finder/ Database URL:http://54.146.11.205:8484/BELXtractor/finder/
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Affiliation(s)
- K E Ravikumar
- Department of Health Sciences Research, Mayo Clinic, USA and
| | - Majid Rastegar-Mojarad
- Department of Health Sciences Research, Mayo Clinic, USA and.,Department of Health Informatics and Administration, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, USA and
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14
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Jovanović J, Bagheri E. Semantic annotation in biomedicine: the current landscape. J Biomed Semantics 2017; 8:44. [PMID: 28938912 PMCID: PMC5610427 DOI: 10.1186/s13326-017-0153-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 09/17/2017] [Indexed: 01/12/2023] Open
Abstract
The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.
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Affiliation(s)
- Jelena Jovanović
- Department of Software Engineering, University of Belgrade, 154 Jove Ilica Street, Belgrade, Serbia
| | - Ebrahim Bagheri
- Department of Electrical Engineering, Ryerson University, 245 Church Street, Toronto, Canada.
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15
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Hassani-Pak K, Rawlings C. Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes. J Integr Bioinform 2017; 14:/j/jib.ahead-of-print/jib-2016-0002/jib-2016-0002.xml. [PMID: 28609292 PMCID: PMC6042805 DOI: 10.1515/jib-2016-0002] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 02/16/2017] [Indexed: 02/06/2023] Open
Abstract
Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.
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16
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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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17
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Mao Y, Lu Z. MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank. J Biomed Semantics 2017; 8:15. [PMID: 28412964 PMCID: PMC5392968 DOI: 10.1186/s13326-017-0123-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 03/16/2017] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. METHODS We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. RESULTS We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F1-score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. CONCLUSIONS We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. AVAILABILITY http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/ .
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Affiliation(s)
- Yuqing Mao
- Nanjing University of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu, 210023, China
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD, 20894, USA.
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18
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Abstract
In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.
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Affiliation(s)
- Patrick Ruch
- SIB Text Mining, Swiss Institute of Bioinformatics, Geneva, Switzerland.
- BiTeM Group, HES-SO\HEG Genève, 7 route de Drize, CH-1227, Carouge, Switzerland.
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19
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Chang NW, Dai HJ, Shih YY, Wu CY, Dela Rosa MAC, Obena RP, Chen YJ, Hsu WL, Oyang YJ. Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy. Database (Oxford) 2017; 2017:bax082. [PMID: 31725857 PMCID: PMC7243925 DOI: 10.1093/database/bax082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 10/11/2017] [Accepted: 10/11/2017] [Indexed: 12/31/2022]
Abstract
Hepatocellular carcinoma (HCC), one of the most common causes of cancer-related deaths, carries a 5-year survival rate of 18%, underscoring the need for robust biomarkers. In spite of the increased availability of HCC related literatures, many of the promising biomarkers reported have not been validated for clinical use. To narrow down the wide range of possible biomarkers for further clinical validation, bioinformaticians need to sort them out using information provided in published works. Biomedical text mining is an automated way to obtain information of interest within the massive collection of biomedical knowledge, thus enabling extraction of data for biomarkers associated with certain diseases. This method can significantly reduce both the time and effort spent on studying important maladies such as liver diseases. Herein, we report a text mining-aided curation pipeline to identify potential biomarkers for liver cancer. The curation pipeline integrates PubMed E-Utilities to collect abstracts from PubMed and recognize several types of named entities by machine learning-based and pattern-based methods. Genes/proteins from evidential sentences were classified as candidate biomarkers using a convolutional neural network. Lastly, extracted biomarkers were ranked depending on several criteria, such as the frequency of keywords and articles and the journal impact factor, and then integrated into a meaningful list for bioinformaticians. Based on the developed pipeline, we constructed MarkerHub, which contains 2128 candidate biomarkers extracted from PubMed publications from 2008 to 2017. Database URL: http://markerhub.iis.sinica.edu.tw.
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Affiliation(s)
- Nai-Wen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Hong-Jie Dai
- Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan
- Interdisciplinary Program of Green and Information Technology, National Taitung University, Taitung, Taiwan
| | - Yung-Yu Shih
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Chi-Yang Wu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | | | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
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20
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Singhal A, Leaman R, Catlett N, Lemberger T, McEntyre J, Polson S, Xenarios I, Arighi C, Lu Z. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges. Database (Oxford) 2016; 2016:baw161. [PMID: 28025348 PMCID: PMC5199160 DOI: 10.1093/database/baw161] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 11/10/2016] [Accepted: 11/11/2016] [Indexed: 12/24/2022]
Abstract
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.
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Affiliation(s)
- Ayush Singhal
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Robert Leaman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | | | - Johanna McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Shawn Polson
- Center for Bioinformatics and Computational Biology and Department of Computer and Information Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
| | | | - Cecilia Arighi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
- Center for Bioinformatics and Computational Biology and Department of Computer and Information Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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21
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Przybyła P, Shardlow M, Aubin S, Bossy R, Eckart de Castilho R, Piperidis S, McNaught J, Ananiadou S. Text mining resources for the life sciences. Database (Oxford) 2016; 2016:baw145. [PMID: 27888231 PMCID: PMC5199186 DOI: 10.1093/database/baw145] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 10/13/2016] [Accepted: 10/17/2016] [Indexed: 11/18/2022]
Abstract
Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability.
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Affiliation(s)
- Piotr Przybyła
- National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK
| | - Matthew Shardlow
- National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK
| | - Sophie Aubin
- Institut National de la Recherche Agronomique, Jouy-en-Josas, France
| | - Robert Bossy
- Institut National de la Recherche Agronomique, Jouy-en-Josas, France
| | | | - Stelios Piperidis
- Institute for Language and Speech Processing, Athena Research Center, Athens, Greece
| | - John McNaught
- National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK
| | - Sophia Ananiadou
- National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK
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22
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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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Funk CS, Cohen KB, Hunter LE, Verspoor KM. Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition. J Biomed Semantics 2016; 7:52. [PMID: 27613112 PMCID: PMC5018193 DOI: 10.1186/s13326-016-0096-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 08/05/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and how they are expressed in natural language text. The construction of highly specific GO terms is formulaic, consisting of parts and pieces from more simple terms. RESULTS We present two different types of manually generated rules to help capture the variation of how GO terms can appear in natural language text. The first set of rules takes into account the compositional nature of GO and recursively decomposes the terms into their smallest constituent parts. The second set of rules generates derivational variations of these smaller terms and compositionally combines all generated variants to form the original term. By applying both types of rules, new synonyms are generated for two-thirds of all GO terms and an increase in F-measure performance for recognition of GO on the CRAFT corpus from 0.498 to 0.636 is observed. Additionally, we evaluated the combination of both types of rules over one million full text documents from Elsevier; manual validation and error analysis show we are able to recognize GO concepts with reasonable accuracy (88 %) based on random sampling of annotations. CONCLUSIONS In this work we present a set of simple synonym generation rules that utilize the highly compositional and formulaic nature of the Gene Ontology concepts. We illustrate how the generated synonyms aid in improving recognition of GO concepts on two different biomedical corpora. We discuss other applications of our rules for GO ontology quality assurance, explore the issue of overgeneration, and provide examples of how similar methodologies could be applied to other biomedical terminologies. Additionally, we provide all generated synonyms for use by the text-mining community.
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Affiliation(s)
- Christopher S. Funk
- Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - K. Bretonnel Cohen
- Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - Lawrence E. Hunter
- Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - Karin M. Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010 Australia
- Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Melbourne, 3010 Australia
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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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Rinaldi F, Ellendorff TR, Madan S, Clematide S, van der Lek A, Mevissen T, Fluck J. BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw067. [PMID: 27402677 PMCID: PMC4940434 DOI: 10.1093/database/baw067] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 04/11/2016] [Indexed: 12/27/2022]
Abstract
Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.
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Affiliation(s)
- Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | | | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Simon Clematide
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Adrian van der Lek
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Theo Mevissen
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
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Henrique T, José Freitas da Silveira N, Henrique Cunha Volpato A, Mioto MM, Carolina Buzzo Stefanini A, Bachir Fares A, Gustavo da Silva Castro Andrade J, Masson C, Verónica Mendoza López R, Daumas Nunes F, Paulo Kowalski L, Severino P, Tajara EH. HNdb: an integrated database of gene and protein information on head and neck squamous cell carcinoma. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw026. [PMID: 27013077 PMCID: PMC4806539 DOI: 10.1093/database/baw026] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 02/19/2016] [Indexed: 12/19/2022]
Abstract
The total amount of scientific literature has grown rapidly in recent years. Specifically, there are several million citations in the field of cancer. This makes it difficult, if not impossible, to manually retrieve relevant information on the mechanisms that govern tumor behavior or the neoplastic process. Furthermore, cancer is a complex disease or, more accurately, a set of diseases. The heterogeneity that permeates many tumors is particularly evident in head and neck (HN) cancer, one of the most common types of cancer worldwide. In this study, we present HNdb, a free database that aims to provide a unified and comprehensive resource of information on genes and proteins involved in HN squamous cell carcinoma, covering data on genomics, transcriptomics, proteomics, literature citations and also cross-references of external databases. Different literature searches of MEDLINE abstracts were performed using specific Medical Subject Headings (MeSH terms) for oral, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinomas. A curated gene-to-publication assignment yielded a total of 1370 genes related to HN cancer. The diversity of results allowed identifying novel and mostly unexplored gene associations, revealing,for example, that processes linked to response to steroid hormone stimulus are significantly enriched in genes related to HN carcinomas. Thus, our database expands the possibilities for gene networks investigation, providing potential hypothesis to be tested. Database URL:http://www.gencapo.famerp.br/hndb.
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Affiliation(s)
- Tiago Henrique
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - Nelson José Freitas da Silveira
- Institute of Exact Science, Federal University of Alfenas, MG, Brazil, Rua Gabriel Monteiro da Silva, 700 Centro 37130-000 - Alfenas, MG - Brazil
| | - Arthur Henrique Cunha Volpato
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - Mayra Mataruco Mioto
- Department of Dermatological, Infectious, and Parasitic Diseases, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - Ana Carolina Buzzo Stefanini
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, SP, Brazil R. do Matão Butantã 05508-090 - São Paulo, SP, Brazil
| | - Adil Bachir Fares
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - João Gustavo da Silva Castro Andrade
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - Carolina Masson
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil
| | - Rossana Verónica Mendoza López
- State of São Paulo Cancer Institute - ICESP, SP, Brazil Av. Dr. Arnaldo, 251 Pacaembu 01246-000 - São Paulo, SP - Brazil
| | - Fabio Daumas Nunes
- Department of Stomatology School of Dentistry, University of São Paulo, SP, Brazil Avenida Professor Lineu Prestes, 2227 Butantã 05508-000 - São Paulo, SP - Brazil
| | - Luis Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, Cancer Hospital A.C. Camargo, SP, Brazil Rua Prof Antonio Prudente, 211 Liberdade 01509-010 - São Paulo, SP - Brazil and
| | - Patricia Severino
- Albert Einstein Research and Education Institute, Hospital Israelita Albert Einstein, SP, Brazil Av. Albert Einstein, 627 Morumbi 05652-000 - São Paulo, SP - Brazil
| | - Eloiza Helena Tajara
- Department of Molecular Biology, School of Medicine of São José do Rio Preto, SP, Brazil Av Brigadeiro Faria Lima n° 5416 Vila Sao Pedro 15090-000 - São José do Rio Preto, SP - Brazil Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, SP, Brazil R. do Matão Butantã 05508-090 - São Paulo, SP, Brazil
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Koestler SA, Alaybeyoglu B, Weichenberger CX, Celik A. FlyOde - a platform for community curation and interactive visualization of dynamic gene regulatory networks in Drosophila eye development. F1000Res 2015; 4:1484. [PMID: 26998229 PMCID: PMC4786896 DOI: 10.12688/f1000research.7556.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/16/2015] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Understanding the regulatory mechanisms governing eye development of the model organism Drosophila melanogaster (D. m.) requires structured knowledge of the involved genes and proteins, their interactions, and dynamic expression patterns. Especially the latter information is however to a large extent scattered throughout the literature. RESULTS FlyOde is an online platform for the systematic assembly of data on D. m. eye development. It consists of data on eye development obtained from the literature, and a web interface for users to interactively display these data as a gene regulatory network. Our manual curation process provides high standard structured data, following a specifically designed ontology. Visualization of gene interactions provides an overview of network topology, and filtering according to user-defined expression patterns makes it a versatile tool for daily tasks, as demonstrated by usage examples. Users are encouraged to submit additional data via a simple online form.
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Affiliation(s)
- Stefan A Koestler
- Department of Molecular Biology and Genetics, Bogazici University, Istanbul, 34342, Turkey
| | - Begum Alaybeyoglu
- Department of Chemical Engineering, Bogazici University, Istanbul, 34342, Turkey
| | - Christian X Weichenberger
- Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), (Affiliated to the University of Lübeck, Lübeck, Germany), Bozen/Bolzano, 39100, Italy
| | - Arzu Celik
- Department of Molecular Biology and Genetics, Bogazici University, Istanbul, 34342, Turkey.,Life Sciences Center, Bogazici University, Istanbul, 34342, Turkey
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28
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Hoehndorf R, Schofield PN, Gkoutos GV. The role of ontologies in biological and biomedical research: a functional perspective. Brief Bioinform 2015; 16:1069-80. [PMID: 25863278 PMCID: PMC4652617 DOI: 10.1093/bib/bbv011] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 01/20/2015] [Indexed: 12/19/2022] Open
Abstract
Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.
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29
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Di Lena P, Domeniconi G, Margara L, Moro G. GOTA: GO term annotation of biomedical literature. BMC Bioinformatics 2015; 16:346. [PMID: 26511083 PMCID: PMC4625458 DOI: 10.1186/s12859-015-0777-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 10/13/2015] [Indexed: 12/12/2022] Open
Abstract
Background Functional annotation of genes and gene products is a major challenge in the post-genomic era. Nowadays, gene function curation is largely based on manual assignment of Gene Ontology (GO) annotations to genes by using published literature. The annotation task is extremely time-consuming, therefore there is an increasing interest in automated tools that can assist human experts. Results Here we introduce GOTA, a GO term annotator for biomedical literature. The proposed approach makes use only of information that is readily available from public repositories and it is easily expandable to handle novel sources of information. We assess the classification capabilities of GOTA on a large benchmark set of publications. The overall performances are encouraging in comparison to the state of the art in multi-label classification over large taxonomies. Furthermore, the experimental tests provide some interesting insights into the potential improvement of automated annotation tools. Conclusions GOTA implements a flexible and expandable model for GO annotation of biomedical literature. The current version of the GOTA tool is freely available at http://gota.apice.unibo.it. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0777-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy.
| | - Giacomo Domeniconi
- Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy.
| | - Luciano Margara
- Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy.
| | - Gianluca Moro
- Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy.
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GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains. BIOMED RESEARCH INTERNATIONAL 2015; 2015:918710. [PMID: 26380306 PMCID: PMC4561873 DOI: 10.1155/2015/918710] [Citation(s) in RCA: 111] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 04/03/2015] [Accepted: 04/04/2015] [Indexed: 02/01/2023]
Abstract
The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator.
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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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López Villar E, Madero L, A López-Pascual J, C Cho W. Study of phosphorylation events for cancer diagnoses and treatment. Clin Transl Med 2015; 4:59. [PMID: 26055493 PMCID: PMC4460185 DOI: 10.1186/s40169-015-0059-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 05/19/2015] [Indexed: 02/07/2023] Open
Abstract
The activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation and survival, is orchestrated by protein kinases via phosphorylation. A critical issue is the study of the mechanisms of cancer cells for the development of more effective drugs. With the application of the new proteomic technologies, together with the advancement in the sequencing of the human proteome, patients will therefore be benefited by the discovery of novel therapeutic and/or diagnostic protein targets. Furthermore, the advances in proteomic approaches and the Human Proteome Organization (HUPO) have opened a new door which is helpful in the identification of patients at risk and towards improving current therapies. Modification of the signaling-networks via mutations or abnormal protein expression underlies the cause or consequence of many diseases including cancer. Resulting data is used to reveal connections between genes proteins and compounds and the related molecular pathways for underlining disease states. As a delegate of HUPO, for human proteome on children assays and studies, we, at Hospital Universitario Niño Jesús, are seeking to support the human proteome in this context. Clinical goals have to be clearly established and proteomics experts have to set up the appropriate proteomic strategy, which coupled to bioinformatics will make it possible to achieve new therapies for patients with poor prognosis. We envision to combine our up-coming data to the HUPO organization in order to support international efforts to advance the cure of cancer disease.
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Affiliation(s)
- Elena López Villar
- Oncohematology of Children Department, Hospital Universitario Infantil Niño Jesús, Av. Menéndez Pelayo 65, Madrid, Spain,
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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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Feinberg JI, Bakulski KM, Jaffe AE, Tryggvadottir R, Brown SC, Goldman LR, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Fallin MD, Feinberg AP. Paternal sperm DNA methylation associated with early signs of autism risk in an autism-enriched cohort. Int J Epidemiol 2015; 44:1199-210. [PMID: 25878217 DOI: 10.1093/ije/dyv028] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2015] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Epigenetic mechanisms such as altered DNA methylation have been suggested to play a role in autism, beginning with the classical association of Prader-Willi syndrome, an imprinting disorder, with autistic features. OBJECTIVES Here we tested for the relationship of paternal sperm DNA methylation with autism risk in offspring, examining an enriched-risk cohort of fathers of autistic children. METHODS We examined genome-wide DNA methylation (DNAm) in paternal semen biosamples obtained from an autism spectrum disorder (ASD) enriched-risk pregnancy cohort, the Early Autism Risk Longitudinal Investigation (EARLI) cohort, to estimate associations between sperm DNAm and prospective ASD development, using a 12-month ASD symptoms assessment, the Autism Observation Scale for Infants (AOSI). We analysed methylation data from 44 sperm samples run on the CHARM 3.0 array, which contains over 4 million probes (over 7 million CpG sites), including 30 samples also run on the Illumina Infinium HumanMethylation450 (450K) BeadChip platform (∼485 000 CpG sites). We also examined associated regions in an independent sample of post-mortem human brain ASD and control samples for which Illumina 450K DNA methylation data were available. RESULTS Using region-based statistical approaches, we identified 193 differentially methylated regions (DMRs) in paternal sperm with a family-wise empirical P-value [family-wise error rate (FWER)] <0.05 associated with performance on the Autism Observational Scale for Infants (AOSI) at 12 months of age in offspring. The DMRs clustered near genes involved in developmental processes, including many genes in the SNORD family, within the Prader-Willi syndrome gene cluster. These results were consistent among the 75 probes on the Illumina 450K array that cover AOSI-associated DMRs from CHARM. Further, 18 of 75 (24%) 450K array probes showed consistent differences in the cerebellums of autistic individuals compared with controls. CONCLUSIONS These data suggest that epigenetic differences in paternal sperm may contribute to autism risk in offspring, and provide evidence that directionally consistent, potentially related epigenetic mechanisms may be operating in the cerebellum of individuals with autism.
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Affiliation(s)
- Jason I Feinberg
- Johns Hopkins Bloomberg School of Public Health, Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins University, Center for Epigenetics
| | - Kelly M Bakulski
- Johns Hopkins Bloomberg School of Public Health, Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins University, Center for Epigenetics, Johns Hopkins Bloomberg School of Public Health, Epidemiology
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Bloomberg School of Public Health, Mental Health and
| | | | - Shannon C Brown
- Johns Hopkins Bloomberg School of Public Health, Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Epidemiology
| | - Lynn R Goldman
- George Washington University, Milken Institute School of Public Health, Johns Hopkins Bloomberg School of Public Health
| | - Lisa A Croen
- Kaiser Permanente, Division of Research, Autism Research Program
| | | | - Craig J Newschaffer
- Drexel University, A.J. Drexel Autism Institute, Drexel University School of Public Health, Epidemiology and Biostatistics
| | - M Daniele Fallin
- Johns Hopkins Bloomberg School of Public Health, Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Mental Health and
| | - Andrew P Feinberg
- Johns Hopkins University, Center for Epigenetics, Johns Hopkins University School of Medicine, Medicine
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Li Y, Liu H. Learning Semantic Tags from Big Data for Clinical Text Representation. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2015; 2015:461-5. [PMID: 26306286 PMCID: PMC4525271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.
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Khare R, Burger JD, Aberdeen JS, Tresner-Kirsch DW, Corrales TJ, Hirchman L, Lu Z. Scaling drug indication curation through crowdsourcing. Database (Oxford) 2015; 2015:bav016. [PMID: 25797061 PMCID: PMC4369375 DOI: 10.1093/database/bav016] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2014] [Revised: 02/04/2015] [Accepted: 02/09/2015] [Indexed: 01/24/2023]
Abstract
Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed LabeledIn, a human-curated drug indication resource for 250 clinical drugs. Its development required over 40 h of human effort across 20 weeks, despite using well-defined annotation guidelines. In this study, we aim to investigate the feasibility of scaling drug indication annotation through a crowdsourcing technique where an unknown network of workers can be recruited through the technical environment of Amazon Mechanical Turk (MTurk). To translate the expert-curation task of cataloging indications into human intelligence tasks (HITs) suitable for the average workers on MTurk, we first simplify the complex task such that each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts.
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Affiliation(s)
- Ritu Khare
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - John D Burger
- The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA
| | - John S Aberdeen
- The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA
| | | | - Theodore J Corrales
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA, Montgomery Blair High School, 57 University Blvd E., Silver Spring, MD 20901, USA
| | - Lynette Hirchman
- The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20894, USA.
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37
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Funk CS, Kahanda I, Ben-Hur A, Verspoor KM. Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct. J Biomed Semantics 2015; 6:9. [PMID: 26005564 PMCID: PMC4441003 DOI: 10.1186/s13326-015-0006-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 02/27/2015] [Indexed: 11/10/2022] Open
Abstract
Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a “medium-throughput” pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.
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Affiliation(s)
- Christopher S Funk
- Computational Bioscience Program, University of Colorado School of Medicine, Aurora, 80045 CO USA
| | - Indika Kahanda
- Department of Computer Science, Colorado State University, Fort Collins, 80523 CO USA
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, 80523 CO USA
| | - Karin M Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, 3010 Victoria Australia ; Health and Biomedical Informatics Centre, University of Melbourne, Parkville, 3010 Victoria Australia
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38
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Liu W, Laulederkind SJF, Hayman GT, Wang SJ, Nigam R, Smith JR, De Pons J, Dwinell MR, Shimoyama M. OntoMate: a text-mining tool aiding curation at the Rat Genome Database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bau129. [PMID: 25619558 PMCID: PMC4305386 DOI: 10.1093/database/bau129] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The Rat Genome Database (RGD) is the premier repository of rat genomic, genetic and physiologic data. Converting data from free text in the scientific literature to a structured format is one of the main tasks of all model organism databases. RGD spends considerable effort manually curating gene, Quantitative Trait Locus (QTL) and strain information. The rapidly growing volume of biomedical literature and the active research in the biological natural language processing (bioNLP) community have given RGD the impetus to adopt text-mining tools to improve curation efficiency. Recently, RGD has initiated a project to use OntoMate, an ontology-driven, concept-based literature search engine developed at RGD, as a replacement for the PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search engine in the gene curation workflow. OntoMate tags abstracts with gene names, gene mutations, organism name and most of the 16 ontologies/vocabularies used at RGD. All terms/ entities tagged to an abstract are listed with the abstract in the search results. All listed terms are linked both to data entry boxes and a term browser in the curation tool. OntoMate also provides user-activated filters for species, date and other parameters relevant to the literature search. Using the system for literature search and import has streamlined the process compared to using PubMed. The system was built with a scalable and open architecture, including features specifically designed to accelerate the RGD gene curation process. With the use of bioNLP tools, RGD has added more automation to its curation workflow. Database URL:http://rgd.mcw.edu
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Affiliation(s)
- Weisong Liu
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Stanley J F Laulederkind
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - G Thomas Hayman
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Shur-Jen Wang
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Rajni Nigam
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Jennifer R Smith
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Jeff De Pons
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Melinda R Dwinell
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
| | - Mary Shimoyama
- Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA
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Zhu D, Li D, Carterette B, Liu H. Integrating information retrieval with distant supervision for gene ontology annotation. Database (Oxford) 2014; 2014:bau087. [PMID: 25183856 PMCID: PMC4150992 DOI: 10.1093/database/bau087] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2014] [Revised: 07/30/2014] [Accepted: 07/30/2014] [Indexed: 01/08/2023]
Abstract
This article describes our participation of the Gene Ontology Curation task (GO task) in BioCreative IV where we participated in both subtasks: A) identification of GO evidence sentences (GOESs) for relevant genes in full-text articles and B) prediction of GO terms for relevant genes in full-text articles. For subtask A, we trained a logistic regression model to detect GOES based on annotations in the training data supplemented with more noisy negatives from an external resource. Then, a greedy approach was applied to associate genes with sentences. For subtask B, we designed two types of systems: (i) search-based systems, which predict GO terms based on existing annotations for GOESs that are of different textual granularities (i.e., full-text articles, abstracts, and sentences) using state-of-the-art information retrieval techniques (i.e., a novel application of the idea of distant supervision) and (ii) a similarity-based system, which assigns GO terms based on the distance between words in sentences and GO terms/synonyms. Our best performing system for subtask A achieves an F1 score of 0.27 based on exact match and 0.387 allowing relaxed overlap match. Our best performing system for subtask B, a search-based system, achieves an F1 score of 0.075 based on exact match and 0.301 considering hierarchical matches. Our search-based systems for subtask B significantly outperformed the similarity-based system. DATABASE URL https://github.com/noname2020/Bioc.
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Affiliation(s)
- Dongqing Zhu
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905 and Department of Computer & Information Sciences, University of Delaware, 101 SMITH HALL, Newark, DE 19716, USA Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905 and Department of Computer & Information Sciences, University of Delaware, 101 SMITH HALL, Newark, DE 19716, USA
| | - Dingcheng Li
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905 and Department of Computer & Information Sciences, University of Delaware, 101 SMITH HALL, Newark, DE 19716, USA
| | - Ben Carterette
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905 and Department of Computer & Information Sciences, University of Delaware, 101 SMITH HALL, Newark, DE 19716, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905 and Department of Computer & Information Sciences, University of Delaware, 101 SMITH HALL, Newark, DE 19716, USA
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40
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Qin T, Matmati N, Tsoi LC, Mohanty BK, Gao N, Tang J, Lawson AB, Hannun YA, Zheng WJ. Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network. Nucleic Acids Res 2014; 42:e138. [PMID: 25063300 PMCID: PMC4191379 DOI: 10.1093/nar/gku678] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general.
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Affiliation(s)
- Tingting Qin
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nabil Matmati
- The Stony Brook University Cancer Center and the Department of Medicine, Stony Brook, NY 11794, USA
| | - Lam C Tsoi
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bidyut K Mohanty
- Department of Biochemistry & Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Nan Gao
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Jijun Tang
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Andrew B Lawson
- Department of Public Health Science, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Yusuf A Hannun
- The Stony Brook University Cancer Center and the Department of Medicine, Stony Brook, NY 11794, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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41
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Comeau DC, Batista-Navarro RT, Dai HJ, Doğan RI, Yepes AJ, Khare R, Lu Z, Marques H, Mattingly CJ, Neves M, Peng Y, Rak R, Rinaldi F, Tsai RTH, Verspoor K, Wiegers TC, Wu CH, Wilbur WJ. BioC interoperability track overview. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau053. [PMID: 24980129 PMCID: PMC4074764 DOI: 10.1093/database/bau053] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BioC is a new simple XML format for sharing biomedical text and annotations and libraries to read and write that format. This promotes the development of interoperable tools for natural language processing (NLP) of biomedical text. The interoperability track at the BioCreative IV workshop featured contributions using or highlighting the BioC format. These contributions included additional implementations of BioC, many new corpora in the format, biomedical NLP tools consuming and producing the format and online services using the format. The ease of use, broad support and rapidly growing number of tools demonstrate the need for and value of the BioC format. Database URL:http://bioc.sourceforge.net/
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Affiliation(s)
- Donald C Comeau
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Riza Theresa Batista-Navarro
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Hong-Jie Dai
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Rezarta Islamaj Doğan
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Antonio Jimeno Yepes
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Ritu Khare
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Hernani Marques
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Carolyn J Mattingly
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Mariana Neves
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USANational Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Univers
| | - Yifan Peng
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Rafal Rak
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Fabio Rinaldi
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Richard Tzong-Han Tsai
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Karin Verspoor
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USANational Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Univers
| | - Thomas C Wiegers
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Cathy H Wu
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USANational Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Univers
| | - W John Wilbur
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester M1 7DN, UK, Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C., Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria Australia 3010, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, USA, WBI, Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany, Berlin Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin 13353, Germany, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan, R.O.C., Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Victoria Australia 3010, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
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