{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T09:57:17Z","timestamp":1763978237967,"version":"build-2065373602"},"reference-count":146,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["POCI-01-0145-FEDER-031356","UIDB\/04539\/2020","PD\/BD\/135179\/2017","2020.05718.BD","2020.07766.BD"],"award-info":[{"award-number":["POCI-01-0145-FEDER-031356","UIDB\/04539\/2020","PD\/BD\/135179\/2017","2020.05718.BD","2020.07766.BD"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000921","name":"European Cooperation in Science and Technology","doi-asserted-by":"publisher","award":["CA17104"],"award-info":[{"award-number":["CA17104"]}],"id":[{"id":"10.13039\/501100000921","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BioChem"],"abstract":"<jats:p>Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms\u2019 application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.<\/jats:p>","DOI":"10.3390\/biochem1020007","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T12:18:31Z","timestamp":1627388311000},"page":"60-80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7225-9287","authenticated-orcid":false,"given":"N\u00edcia","family":"Ros\u00e1rio-Ferreira","sequence":"first","affiliation":[{"name":"CQC-Coimbra Chemistry Center, Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535 Coimbra, Portugal"},{"name":"CIBB, University of Coimbra, 3000-456 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-8991","authenticated-orcid":false,"given":"Catarina","family":"Marques-Pereira","sequence":"additional","affiliation":[{"name":"CIBB, University of Coimbra, 3000-456 Coimbra, Portugal"},{"name":"IIIs-Institute for Interdisciplinary Research, University of Coimbra, 3000-456 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-0787","authenticated-orcid":false,"given":"Manuel","family":"Pires","sequence":"additional","affiliation":[{"name":"CIBB, University of Coimbra, 3000-456 Coimbra, Portugal"},{"name":"Department of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9611-6889","authenticated-orcid":false,"given":"Daniel","family":"Ramalh\u00e3o","sequence":"additional","affiliation":[{"name":"CIBB, University of Coimbra, 3000-456 Coimbra, Portugal"},{"name":"Department of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7850-9929","authenticated-orcid":false,"given":"N\u00e1dia","family":"Pereira","sequence":"additional","affiliation":[{"name":"CIBB, University of Coimbra, 3000-456 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-5618","authenticated-orcid":false,"given":"Victor","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Department of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"INESC-TEC-Centre of Advanced Computing Systems, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3344-8237","authenticated-orcid":false,"given":"V\u00edtor","family":"Santos Costa","sequence":"additional","affiliation":[{"name":"Department of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"INESC-TEC-Centre of Advanced Computing Systems, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2970-5250","authenticated-orcid":false,"given":"Irina Sousa","family":"Moreira","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, University of Coimbra, Cal\u00e7ada Martim de Freitas, 3000-456 Coimbra, Portugal"},{"name":"CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-535 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1111\/phn.12809","article-title":"Mining twitter to explore the emergence of COVID-19 symptoms","volume":"37","author":"Guo","year":"2020","journal-title":"Public Health Nurs."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e112","DOI":"10.1136\/tobaccocontrol-2016-053295","article-title":"Public reactions to e-cigarette regulations on Twitter: A text mining analysis","volume":"26","author":"Lazard","year":"2017","journal-title":"Tobacco Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e18350","DOI":"10.2196\/18350","article-title":"Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis","volume":"22","author":"Nasralah","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bach, M.P., Krsti\u0107, \u017d., Seljan, S., and Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11.","DOI":"10.3390\/su11051277"},{"key":"ref_5","first-page":"705","article-title":"Information retrieval and terminology extraction in online resources for patients with diabetes","volume":"38","author":"Seljan","year":"2014","journal-title":"Coll. Antropol."},{"key":"ref_6","unstructured":"Seljan, S., Dun\u0111er, I., and Stan\u010di\u0107, H. (2017). Extracting Terminology by Language Independent Methods. Forum Translationswissenschaft: Translation Studies and Translation Practice 19, Peter Lang D."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ymeth.2015.01.015","article-title":"Application of text mining in the biomedical domain","volume":"74","author":"Fleuren","year":"2015","journal-title":"Methods"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shorten, C., Khoshgoftaar, T.M., and Furht, B. (2021). Deep Learning applications for COVID-19. J. Big Data, 8.","DOI":"10.1186\/s40537-020-00392-9"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e18","DOI":"10.5808\/GI.2019.17.2.e18","article-title":"A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition","volume":"17","author":"Gachloo","year":"2019","journal-title":"Genom. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, S., Dharssi, S., Wu, M., Li, J., and Lu, Z. (2019). Text Mining for Drug Discovery. Methods in Molecular Biology, Springer.","DOI":"10.1007\/978-1-4939-9089-4_13"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/bib\/bbv087","article-title":"Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery","volume":"17","author":"Gonzalez","year":"2015","journal-title":"Briefings Bioinform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jbi.2012.10.007","article-title":"Biomedical text mining and its applications in cancer research","volume":"46","author":"Zhu","year":"2013","journal-title":"J. Biomed. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3389\/fcell.2020.00673","article-title":"Named Entity Recognition and Relation Detection for Biomedical Information Extraction","volume":"8","author":"Perera","year":"2020","journal-title":"Front. Cell Dev. Biol."},{"key":"ref_14","unstructured":"Beheshti, S.M.R., Venugopal, S., Ryu, S.H., Benatallah, B., and Wang, W. (2013). Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Q., Tang, B., Wang, X., Xu, H., Wang, B., and Huang, D. (2017). CNN-based ranking for biomedical entity normalization. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1805-7"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cho, H., Choi, W., and Lee, H. (2017). A method for named entity normalization in biomedical articles: Application to diseases and plants. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1857-8"},{"key":"ref_17","unstructured":"Shirakawa, M., Wang, H., Song, Y., Wang, Z., Nakayama, K., and Hara, T. (2021, June 12). Entity Disambiguation based on a Probabilistic Taxonomy. Technical Report MSR-TR-2011-25. Available online: https:\/\/www.microsoft.com\/en-us\/research\/publication\/entity-disambiguation-based-on-a-probabilistic-taxonomy\/."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gentile, A.L., Zhang, Z., Xia, L., and Iria, J. (2010). Semantic Relatedness Approach for Named Entity Disambiguation. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-642-15850-6_14"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.eswa.2018.02.011","article-title":"Exploiting semantic similarity for named entity disambiguation in knowledge graphs","volume":"101","author":"Zhu","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yadav, S., Ramesh, S., Saha, S., and Ekbal, A. (2020). Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework. IEEE\/ACM Trans. Comput. Biol. Bioinform.","DOI":"10.1109\/TCBB.2020.3020016"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymeth.2019.02.021","article-title":"Exploring semi-supervised variational autoencoders for biomedical relation extraction","volume":"166","author":"Zhang","year":"2019","journal-title":"Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/910423","article-title":"A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set","volume":"2015","author":"Muzaffar","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xing, R., Luo, J., and Song, T. (2020). BioRel: Towards large-scale biomedical relation extraction. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03889-5"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shah, P., Perez-Iratxeta, C., Bork, P., and Andrade, M. (2003). Information extraction from full text scientific articles: Where are the keywords?. BMC Bioinform., 4.","DOI":"10.1186\/1471-2105-4-20"},{"key":"ref_25","unstructured":"Dai, H., Wu, C.Y., Tzong, R., Tsai, R.T.H., and Hsu, W.L. (2012, January 12\u201315). From Entity Recognition to Entity Linking: A Survey of Advanced Entity Linking Techniques. Proceedings of the 26th Annual Conference of the Japanese Society for Artificial Intelligence, Tokyo, Japan."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Collovini, S., Bonamigo, T., and Vieira, R. (2013). A review on Relation Extraction with an eye on Portuguese. J. Braz. Comput. Soc., 19.","DOI":"10.1007\/s13173-013-0116-8"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/4302425","article-title":"Data Processing and Text Mining Technologies on Electronic Medical Records: A Review","volume":"2018","author":"Sun","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_28","unstructured":"Ghamami, F., and Keyvanpour, M. (2018). Why biomedical relation extraction is an open issue?. ICIC Express Lett. Part B Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saffer, J.D., and Burnett, V.L. (2014). Introduction to Biomedical Literature Text Mining: Context and Objectives. Methods in Molecular Biology, Springer.","DOI":"10.1007\/978-1-4939-0709-0_1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1016\/j.csbj.2020.05.017","article-title":"Constructing knowledge graphs and their biomedical applications","volume":"18","author":"Nicholson","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_31","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_32","unstructured":"Sachan, D.S., Xie, P., and Xing, E.P. (2017). Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition. arXiv."},{"key":"ref_33","unstructured":"Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv.","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., and Kang, J. (2019). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics.","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dustin Wright, Y.K. (2021, June 12). NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction. Available online: https:\/\/openreview.net\/forum?id=BJerQWcp6Q.","DOI":"10.1101\/2022.04.14.488416"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1093\/bib\/bbz075","article-title":"Community curation of bioinformatics software and data resources","volume":"21","author":"Ison","year":"2020","journal-title":"Briefings Bioinform."},{"key":"ref_38","unstructured":"Sammartino, J.C., Krallinger, M., and Valencia, A. (2016, January 4\u20135). Annotation Process, Guidelines and Text Corpus of Small Non-Coding RNA Molecules: The MiNCor for MicroRNA Annotations. Proceedings of the Semantic Mining in Biomedicine (SMBM) 2016 CEUR Workshop Proceedings, Potsdam, Germany."},{"key":"ref_39","first-page":"602","article-title":"Text mining for bioinformatics using biomedical literature","volume":"1","author":"Lamurias","year":"2019","journal-title":"Encycl. Bioinform. Comput. Biol."},{"key":"ref_40","first-page":"175","article-title":"Biomedical named entity recognition: A survey of machine-learning tools","volume":"11","author":"Campos","year":"2012","journal-title":"Theory Appl. Adv. Text Min."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1414-x","article-title":"A neural joint model for entity and relation extraction from biomedical text","volume":"18","author":"Li","year":"2017","journal-title":"BMC Bioinform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.tibtech.2010.04.005","article-title":"Event extraction for systems biology by text mining the literature","volume":"28","author":"Ananiadou","year":"2010","journal-title":"Trends Biotechnol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-10-349","article-title":"Construction of an annotated corpus to support biomedical information extraction","volume":"10","author":"Thompson","year":"2009","journal-title":"BMC Bioinform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"i180","DOI":"10.1093\/bioinformatics\/btg1023","article-title":"GENIA corpus\u2014A semantically annotated corpus for bio-textmining","volume":"19","author":"Kim","year":"2003","journal-title":"Bioinformatics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-13-161","article-title":"Concept annotation in the CRAFT corpus","volume":"13","author":"Bada","year":"2012","journal-title":"BMC Bioinform."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2008-9-s2-s2","article-title":"Overview of BioCreative II gene mention recognition","volume":"9","author":"Smith","year":"2008","journal-title":"Genome Biol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","article-title":"NCBI disease corpus: A resource for disease name recognition and concept normalization","volume":"47","author":"Leaman","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-7-S1-S1","article-title":"The CHEMDNER corpus of chemicals and drugs and its annotation principles","volume":"7","author":"Krallinger","year":"2015","journal-title":"J. Cheminformatics"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, J., Sun, Y., Johnson, R.J., Sciaky, D., Wei, C.H., Leaman, R., Davis, A.P., Mattingly, C.J., Wiegers, T.C., and Lu, Z. (2016). BioCreative V CDR task corpus: A resource for chemical disease relation extraction. Database, 2016.","DOI":"10.1093\/database\/baw068"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lee, K., Lee, S., Park, S., Kim, S., Kim, S., Choi, K., Tan, A.C., and Kang, J. (2016). BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations. Database, 2016.","DOI":"10.1093\/database\/baw043"},{"key":"ref_51","unstructured":"Neves, M., Damaschun, A., Kurtz, A., and Leser, U. (2012, January 26). Annotating and evaluating text for stem cell research. Proceedings of the Third Workshop on Building and Evaluation Resources for Biomedical Text Mining (BioTxtM 2012) at Language Resources and Evaluation (LREC), Manchester, UK."},{"key":"ref_52","unstructured":"Krallinger, M., Rabal, O., Louren\u00e7o, A., Perez, M.P., Rodriguez, G.P., Vazquez, M., Leitner, F., Oyarzabal, J., and Valencia, A. (,  2015). Overview of the CHEMDNER patents task. Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, Available online: https:\/\/www.jdb.uzh.ch\/id\/eprint\/37857."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lee, H.J., Shim, S.H., Song, M.R., Lee, H., and Park, J.C. (2013). CoMAGC: A corpus with multi-faceted annotations of gene-cancer relations. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-323"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cohen, K.B., Verspoor, K., Fort, K., Funk, C., Bada, M., Palmer, M., and Hunter, L.E. (2017). The Colorado Richly Annotated Full Text (CRAFT) Corpus: Multi-Model Annotation in the Biomedical Domain. Handbook of Linguistic Annotation, Springer.","DOI":"10.1007\/978-94-024-0881-2_53"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","article-title":"The DDI corpus: An annotated corpus with pharmacological substances and drug\u2013drug interactions","volume":"46","author":"Declerck","year":"2013","journal-title":"J. Biomed. Inform."},{"key":"ref_56","unstructured":"Gerner, M., Nenadic, G., and Bergman, C.M. (2010). An Exploration of Mining Gene Expression Mentions and Their Anatomical Locations from Biomedical Text. Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Association for Computational Linguistics."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5808\/GI.2018.16.3.75","article-title":"GNI Corpus version 1.0: Annotated full-text corpus of Genomics & Informatics to support biomedical information extraction","volume":"16","author":"Oh","year":"2018","journal-title":"Genom. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Smith, L.H., Tanabe, L., Rindflesch, T.C., and Wilbur, W.J. (2005, January 24). MedTag: A collection of biomedical annotations. Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics, Stroudsburg, PA, USA.","DOI":"10.3115\/1641484.1641489"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"i575","DOI":"10.1093\/bioinformatics\/bts407","article-title":"Event extraction across multiple levels of biological organization","volume":"28","author":"Pyysalo","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_60","unstructured":"Shardlow, M., Nguyen, N., Owen, G., O\u2019Donovan, C., Leach, A., McNaught, J., Turner, S., and Ananiadou, S. (2018, January 7\u201312). A new corpus to support text mining for the curation of metabolites in the Chebi database. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-021-00875-1","article-title":"NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature","volume":"8","author":"Islamaj","year":"2021","journal-title":"Sci. Data"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103779","DOI":"10.1016\/j.jbi.2021.103779","article-title":"NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition","volume":"118","author":"Islamaj","year":"2021","journal-title":"J. Biomed. Informatics"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sousa, D., Lam\u00farias, A., and Couto, F.M. (2019). A silver standard corpus of human phenotype-gene relations. arXiv.","DOI":"10.18653\/v1\/N19-1152"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Verspoor, K., Jimeno Yepes, A., Cavedon, L., McIntosh, T., Herten-Crabb, A., Thomas, Z., and Plazzer, J.P. (2013). Annotating the biomedical literature for the human variome. Database, 2013.","DOI":"10.1093\/database\/bat019"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cunningham, H., Tablan, V., Roberts, A., and Bontcheva, K. (2013). Getting More Out of Biomedical Documents with GATE\u2019s Full Lifecycle Open Source Text Analytics. PLoS Comput. Biol., 9.","DOI":"10.1371\/journal.pcbi.1002854"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Johansson, M., Roberts, A., Chen, D., Li, Y., Delahaye-Sourdeix, M., Aswani, N., Greenwood, M.A., Benhamou, S., Lagiou, P., and Holc\u00e1tov\u00e1, I. (2012). Using Prior Information from the Medical Literature in GWAS of Oral Cancer Identifies Novel Susceptibility Variant on Chromosome 4\u2014The AdAPT Method. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0036888"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1017\/S1351324904003523","article-title":"UIMA: An architectural approach to unstructured information processing in the corporate research environment","volume":"10","author":"Ferrucci","year":"2004","journal-title":"Nat. Lang. Eng."},{"key":"ref_68","unstructured":"Ogren, P.V., Wetzler, P.G., and Bethard, S. (2008, January 31). ClearTK: A UIMA toolkit for statistical natural language processing. Proceedings of the Towards Enhanced Interoperability for Large HLT Systems: UIMA for NLP Workshop at Language Resources and Evaluation Conference (LREC), Marrakech, Morocco."},{"key":"ref_69","first-page":"3289","article-title":"ClearTK 2.0: Design patterns for machine learning in UIMA","volume":"Volume 2014","author":"Bethard","year":"2014","journal-title":"Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wang, Y., Mehrabi, S., Sohn, S., Atkinson, E.J., Amin, S., and Liu, H. (2019). Natural language processing of radiology reports for identification of skeletal site-specific fractures. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0780-5"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1093\/bioinformatics\/btq250","article-title":"A UIMA wrapper for the NCBO annotator","volume":"26","author":"Roeder","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"bat064","DOI":"10.1093\/database\/bat064","article-title":"BioC: A minimalist approach to interoperability for biomedical text processing","volume":"2013","author":"Comeau","year":"2013","journal-title":"Database"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.1093\/bioinformatics\/btt474","article-title":"DNorm: Disease name normalization with pairwise learning to rank","volume":"29","author":"Leaman","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1093\/bioinformatics\/btt156","article-title":"tmVar: A text mining approach for extracting sequence variants in biomedical literature","volume":"29","author":"Wei","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wei, C.H., Kao, H.Y., and Lu, Z. (2012). SR4GN: A species recognition software tool for gene normalization. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0038460"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-7-S1-S3","article-title":"tmChem: A high performance approach for chemical named entity recognition and normalization","volume":"7","author":"Leaman","year":"2015","journal-title":"J. Cheminformatics"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-S8-S5","article-title":"Cross-species gene normalization by species inference","volume":"12","author":"Wei","year":"2011","journal-title":"BMC Bioinform."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"W518","DOI":"10.1093\/nar\/gkt441","article-title":"PubTator: A web-based text mining tool for assisting biocuration","volume":"41","author":"Wei","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Khare, R., Wei, C.H., Mao, Y., Leaman, R., and Lu, Z. (2014). tmBioC: Improving interoperability of text-mining tools with BioC. Database, 2014.","DOI":"10.1093\/database\/bau073"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Rinaldi, F., Clematide, S., Marques, H., Ellendorff, T., Romacker, M., and Rodriguez-Esteban, R. (2014). OntoGene web services for biomedical text mining. BMC Bioinform., 15.","DOI":"10.1186\/1471-2105-15-S14-S6"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"bau081","DOI":"10.1093\/database\/bau081","article-title":"RLIMS-P: An online text-mining tool for literature-based extraction of protein phosphorylation information","volume":"2014","author":"Torii","year":"2014","journal-title":"Database"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Casteleiro, M.A., Demetriou, G., Read, W., Prieto, M.J.F., Maroto, N., Fernandez, D.M., Nenadic, G., Klein, J., Keane, J., and Stevens, R. (2018). Deep learning meets ontologies: Experiments to anchor the cardiovascular disease ontology in the biomedical literature. J. Biomed. Semant., 9.","DOI":"10.1186\/s13326-018-0181-1"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"baw147","DOI":"10.1093\/database\/baw147","article-title":"The BioC-BioGRID corpus: Full text articles annotated for curation of protein\u2013protein and genetic interactions","volume":"2017","author":"Kim","year":"2017","journal-title":"Database"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., and McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics.","DOI":"10.3115\/v1\/P14-5010"},{"key":"ref_85","first-page":"1208","article-title":"How Do General-Purpose Sentiment Analyzers Perform when Applied to Health-Related Online Social Media Data?","volume":"264","author":"Lu","year":"2019","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1093\/bioinformatics\/btz528","article-title":"HUNER: Improving biomedical NER with pretraining","volume":"36","author":"Weber","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Weber, L., S\u00e4nger, M., M\u00fcnchmeyer, J., Habibi, M., Leser, U., and Akbik, A. (2021). HunFlair: An easy-to-use tool for state-of-the-art biomedical named entity recognition. Bioinformatics.","DOI":"10.1093\/bioinformatics\/btab042"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"103176","DOI":"10.1016\/j.jbi.2019.103176","article-title":"Cimind: A phonetic-based tool for multilingual named entity recognition in biomedical texts","volume":"94","author":"Cabot","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1093\/bioinformatics\/btw234","article-title":"SETH detects and normalizes genetic variants in text","volume":"32","author":"Thomas","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"baw091","DOI":"10.1093\/database\/baw091","article-title":"AuDis: An automatic CRF-enhanced disease normalization in biomedical text","volume":"2016","author":"Lee","year":"2016","journal-title":"Database"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Gupta, S., Dingerdissen, H., Ross, K.E., Hu, Y., Wu, C.H., Mazumder, R., and Vijay-Shanker, K. (2018). DEXTER: Disease-Expression Relation Extraction from Text. Database, 2018.","DOI":"10.1093\/database\/bay045"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"D1128","DOI":"10.1093\/nar\/gkx907","article-title":"BioMuta and BioXpress: Mutation and expression knowledgebases for cancer biomarker discovery","volume":"46","author":"Dingerdissen","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"i490","DOI":"10.1093\/bioinformatics\/btaa430","article-title":"PEDL: Extracting protein\u2013protein associations using deep language models and distant supervision","volume":"36","author":"Weber","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"73729","DOI":"10.1109\/ACCESS.2019.2920708","article-title":"A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_95","unstructured":"Malarkodi, C., Pattabhi, R., and Sobha, L.D. (2021, June 12). CLRG ChemNER: A Chemical Named Entity Recognizer@ ChEMU CLEF 2020. Available online: moz-extension:\/\/c64046de-9d28-4e46-a199-807c4d6ae096\/pdf-viewer\/web\/viewer.html?file=http%3A%2F%2Fceur-ws.org%2FVol-2696%2Fpaper236.pdf."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Yoon, W., So, C.H., Lee, J., and Kang, J. (2019). CollaboNet: Collaboration of deep neural networks for biomedical named entity recognition. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2813-6"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1093\/bioinformatics\/bty356","article-title":"D3NER: Biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information","volume":"34","author":"Dang","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_98","first-page":"1","article-title":"GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains","volume":"2015","author":"Wei","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1093\/bioinformatics\/bty449","article-title":"Transfer learning for biomedical named entity recognition with neural networks","volume":"34","author":"Giorgi","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Chauhan, G., McDermott, M., and Szolovits, P. (2019). Reflex: Flexible framework for relation extraction in multiple domains. arXiv.","DOI":"10.18653\/v1\/W19-5004"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1093\/bioinformatics\/btz504","article-title":"Towards reliable named entity recognition in the biomedical domain","volume":"36","author":"Giorgi","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Neumann, M., King, D., Beltagy, I., and Ammar, W. (2019). Scispacy: Fast and robust models for biomedical natural language processing. arXiv.","DOI":"10.18653\/v1\/W19-5034"},{"key":"ref_103","unstructured":"Dao, M.H., and Nguyen, D.Q. (2021, June 12). VinAI at ChEMU 2020: An Accurate System for Named Entity Recognition in Chemical Reactions from Patents. Available online: https:\/\/www.vinai.io\/publication-posts\/vinai-at-chemu-2020-an-accurate-system-for-named-entity-recognition-in-chemical-reactions-from-patents."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"4331","DOI":"10.1093\/bioinformatics\/btaa515","article-title":"Dataset-aware multi-task learning approaches for biomedical named entity recognition","volume":"36","author":"Zuo","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"i37","DOI":"10.1093\/bioinformatics\/btx228","article-title":"Deep learning with word embeddings improves biomedical named entity recognition","volume":"33","author":"Habibi","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"W587","DOI":"10.1093\/nar\/gkz389","article-title":"PubTator central: Automated concept annotation for biomedical full text articles","volume":"47","author":"Wei","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Djekidel, M.N., Rosikiewicz, W., Peng, J.C., Kanneganti, T.D., Hui, Y., Jin, H., Hedges, D., Schreiner, P., Fan, Y., and Wu, G. (2021, June 12). CovidExpress: An Interactive Portal for Intuitive Investigation on SARS-CoV-2 Related Transcriptomes. Available online: https:\/\/www.biorxiv.org\/content\/10.1101\/2021.05.14.444026v1.","DOI":"10.1101\/2021.05.14.444026"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Wu, M., Zhang, Y., Grosser, M., Tipper, S., Venter, D., Lin, H., and Lu, J. (2021). Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics. Front. Res. Metrics Anal., 6.","DOI":"10.3389\/frma.2021.683212"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Desterke, C., Turhan, A.G., Bennaceur-Griscelli, A., and Griscelli, F. (2021). HLA-dependent heterogeneity and macrophage immunoproteasome activation during lung COVID-19 disease. J. Transl. Med., 19.","DOI":"10.1186\/s12967-021-02965-5"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"25","DOI":"10.12688\/wellcomeopenres.10210.1","article-title":"SciLite: A platform for displaying text-mined annotations as a means to link research articles with biological data","volume":"1","author":"Venkatesan","year":"2016","journal-title":"Wellcome Open Res."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Palopoli, N., Iserte, J.A., Chemes, L.B., Marino-Buslje, C., Parisi, G., Gibson, T.J., and Davey, N.E. (2020). The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification. Database, 2020.","DOI":"10.1093\/database\/baaa040"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1107\/S2053230X1901210X","article-title":"Automatic annotation of protein residues in published papers","volume":"75","author":"Firth","year":"2019","journal-title":"Acta Crystallogr. Sect. Struct. Biol. Commun."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"M\u00fcller, H.M., Kenny, E.E., and Sternberg, P.W. (2004). Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature. PLoS Biol., 2.","DOI":"10.1371\/journal.pbio.0020309"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"4531","DOI":"10.1534\/g3.120.401775","article-title":"BioLitMine: Advanced Mining of Biomedical and Biological Literature About Human Genes and Genes from Major Model Organisms","volume":"10","author":"Hu","year":"2020","journal-title":"G3 Genes Genomes Genetics"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"bau048","DOI":"10.1093\/database\/bau048","article-title":"Egas: A collaborative and interactive document curation platform","volume":"2014","author":"Campos","year":"2014","journal-title":"Database"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1093\/bioinformatics\/btt317","article-title":"BeCAS: Biomedical concept recognition services and visualization","volume":"29","author":"Nunes","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1093\/bioinformatics\/bti749","article-title":"BioThesaurus: A web-based thesaurus of protein and gene names","volume":"22","author":"Liu","year":"2005","journal-title":"Bioinformatics"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/8327980","article-title":"Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer","volume":"2017","author":"Sernadela","year":"2017","journal-title":"BioMed Res. Int."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"W535","DOI":"10.1093\/nar\/gkv383","article-title":"PolySearch2: A significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Khan, F., Radovanovic, A., Gojobori, T., and Kaur, M. (2021). IBDDB: A manually curated and text-mining-enhanced database of genes involved in inflammatory bowel disease. Database, 2021.","DOI":"10.1093\/database\/baab022"},{"key":"ref_121","first-page":"1","article-title":"Regulatory Mechanisms of Coicis Semen on Bionetwork of Liver Cancer Based on Network Pharmacology","volume":"2020","author":"Liu","year":"2020","journal-title":"BioMed Res. Int."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1093\/bioinformatics\/btn469","article-title":"FACTA: A text search engine for finding associated biomedical concepts","volume":"24","author":"Tsuruoka","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"i111","DOI":"10.1093\/bioinformatics\/btr214","article-title":"Discovering and visualizing indirect associations between biomedical concepts","volume":"27","author":"Tsuruoka","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"D115","DOI":"10.1093\/nar\/gkh131","article-title":"UniProt: The Universal Protein knowledgebase","volume":"32","author":"Apweiler","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1136\/jamia.1998.0050001","article-title":"The Unified Medical Language System: An Informatics Research Collaboration","volume":"5","author":"Humphreys","year":"1998","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"D521","DOI":"10.1093\/nar\/gkl923","article-title":"HMDB: The Human Metabolome Database","volume":"35","author":"Wishart","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"DrugBank: A knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Le, N., Ho, T., Ho, B., and Tran, D. (2014). A nucleosomal approach to inferring causal relationships of histone modifications. BMC Genom., 15.","DOI":"10.1186\/1471-2164-15-S1-S7"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: Protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"D380","DOI":"10.1093\/nar\/gkv1277","article-title":"STITCH 5: Augmenting protein\u2013chemical interaction networks with tissue and affinity data","volume":"44","author":"Szklarczyk","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: Towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1177\/107385840000600408","article-title":"The Multiplicity of Serotonin Receptors: Uselessly Diverse Molecules or an Embarrassment of Riches?","volume":"6","author":"Roth","year":"2000","journal-title":"Neuroscientist"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"D437","DOI":"10.1093\/nar\/gkaa1038","article-title":"RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences","volume":"49","author":"Burley","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: A major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"D907","DOI":"10.1093\/nar\/gkm948","article-title":"GLIDA: GPCR ligand database for chemical genomics drug discovery database and tools update","volume":"36","author":"Okuno","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"D919","DOI":"10.1093\/nar\/gkm862","article-title":"SuperTarget and Matador: Resources for exploring drug-target relationships","volume":"36","author":"Gunther","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., and Tan, Y. (2019). Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res.","DOI":"10.1093\/nar\/gkz981"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.crtox.2021.03.001","article-title":"CTD anatomy: Analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies","volume":"2","author":"Davis","year":"2021","journal-title":"Curr. Res. Toxicol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"D545","DOI":"10.1093\/nar\/gkaa970","article-title":"KEGG: Integrating viruses and cellular organisms","volume":"49","author":"Kanehisa","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Jassal, B., Matthews, L., Viteri, G., Gong, C., Lorente, P., Fabregat, A., Sidiropoulos, K., Cook, J., Gillespie, M., and Haw, R. (2019). The reactome pathway knowledgebase. Nucleic Acids Res.","DOI":"10.1093\/nar\/gkz1031"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1093\/bib\/bbx085","article-title":"The BioCyc collection of microbial genomes and metabolic pathways","volume":"20","author":"Karp","year":"2017","journal-title":"Briefings Bioinform."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Huang, H.Y., Lin, Y.C.D., Li, J., Huang, K.Y., Shrestha, S., Hong, H.C., Tang, Y., Chen, Y.G., Jin, C.N., and Yu, Y. (2019). miRTarBase 2020: Updates to the experimentally validated microRNA\u2013target interaction database. Nucleic Acids Res.","DOI":"10.1093\/nar\/gkz896"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"D529","DOI":"10.1093\/nar\/gky1079","article-title":"The BioGRID interaction database: 2019 update","volume":"47","author":"Oughtred","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_145","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"Ronzano","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.ipm.2018.11.011","article-title":"Online visibility of software-related web sites: The case of biomedical text mining tools","volume":"56","year":"2019","journal-title":"Inf. Process. Manag."}],"container-title":["BioChem"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6411\/1\/2\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:35:40Z","timestamp":1760164540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6411\/1\/2\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,27]]},"references-count":146,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["biochem1020007"],"URL":"https:\/\/doi.org\/10.3390\/biochem1020007","relation":{},"ISSN":["2673-6411"],"issn-type":[{"type":"electronic","value":"2673-6411"}],"subject":[],"published":{"date-parts":[[2021,7,27]]}}}