{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T21:20:22Z","timestamp":1770499222504,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-025-00400-3","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:32:59Z","timestamp":1763746379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vaner2: towards more general biomedical named entity recognition using multi-task large language model encoders"],"prefix":"10.1007","volume":"14","author":[{"given":"Yuxuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqi","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-5312","authenticated-orcid":false,"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"400_CR1","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baac098","author":"L Weber","year":"2022","unstructured":"Weber L, S\u00e4nger M, Garda S, Barth F, Alt C, Leser U. Chemical-protein relation extraction with ensembles of carefully tuned pretrained language models. Database. 2022. https:\/\/doi.org\/10.1093\/database\/baac098.","journal-title":"Database."},{"issue":"W1","key":"400_CR2","doi-asserted-by":"publisher","first-page":"W540","DOI":"10.1093\/nar\/gkae235","volume":"52","author":"CH Wei","year":"2024","unstructured":"Wei CH, Allot A, Lai PT, Leaman R, Tian S, Luo L, et al. PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Res. 2024;52(W1):W540\u20136. https:\/\/doi.org\/10.1093\/nar\/gkae235.","journal-title":"Nucleic Acids Res."},{"key":"400_CR3","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-01014-w","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Sui X, Pan F, Yu K, Li K, Tian S, et al. A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research. Nat Mach Intell. 2025. https:\/\/doi.org\/10.1038\/s42256-025-01014-w.","journal-title":"Nat Mach Intell."},{"key":"400_CR4","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 2019. pp. 4171\u201386. https:\/\/doi.org\/10.18653\/v1\/N19-1423.","DOI":"10.18653\/v1\/N19-1423"},{"key":"400_CR5","unstructured":"Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, et\u00a0al. Gpt-4 technical report. 2023. arXiv:2303.08774."},{"key":"400_CR6","unstructured":"Grattafiori A, Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, et\u00a0al. The llama 3 herd of models. 2024. arXiv:2407.21783."},{"issue":"9","key":"400_CR7","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1093\/jamia\/ocad259","volume":"31","author":"Y Hu","year":"2024","unstructured":"Hu Y, Chen Q, Du J, Peng X, Keloth VK, Zuo X, et al. Improving large language models for clinical named entity recognition via prompt engineering. J Am Med Inform Assoc. 2024;31(9):1812\u201320. https:\/\/doi.org\/10.1093\/jamia\/ocad259.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"400_CR8","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1038\/s41467-025-56989-2","volume":"16","author":"Q Chen","year":"2025","unstructured":"Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, et al. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nat Commun. 2025;16(1):3280. https:\/\/doi.org\/10.1038\/s41467-025-56989-2.","journal-title":"Nat Commun"},{"issue":"4","key":"400_CR9","doi-asserted-by":"publisher","first-page":"btae163","DOI":"10.1093\/bioinformatics\/btad310","volume":"40","author":"VK Keloth","year":"2024","unstructured":"Keloth VK, Hu Y, Xie Q, Peng X, Wang Y, Zheng A, et al. Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics. 2024;40(4):btae163. https:\/\/doi.org\/10.1093\/bioinformatics\/btad310.","journal-title":"Bioinformatics."},{"key":"400_CR10","unstructured":"Zhou W, Zhang S, Gu Y, Chen M, Poon H. UniversalNER: targeted distillation from large language models for open named entity recognition. In: The twelfth international conference on learning representations; 2024."},{"key":"400_CR11","doi-asserted-by":"publisher","unstructured":"Bian J, Zhai W, Huang X, Zheng J, Zhu S. VANER: leveraging large language model for versatile and adaptive biomedical named entity recognition. In: 27th European conference on artificial intelligence, 19\u201324 October 2024, Santiago de Compostela, Spain\u2013Including 13th conference on prestigious applications of intelligent systems (PAIS 2024); 2024. pp. 1583\u20131590. https:\/\/doi.org\/10.3233\/FAIA240664.","DOI":"10.3233\/FAIA240664"},{"key":"400_CR12","unstructured":"BehnamGhader P, Adlakha V, Mosbach M, Bahdanau D, Chapados N, Reddy S. LLM2Vec: large language models are secretly powerful text encoders. In: first conference on language modeling; 2024."},{"issue":"4","key":"400_CR13","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1093\/bib\/6.4.357","volume":"6","author":"U Leser","year":"2005","unstructured":"Leser U, Hakenberg J. What makes a gene name? Named entity recognition in the biomedical literature. Brief Bioinform. 2005;6(4):357\u201369. https:\/\/doi.org\/10.1093\/bib\/6.4.357.","journal-title":"Brief Bioinform"},{"key":"400_CR14","doi-asserted-by":"publisher","unstructured":"Petasis G, Vichot F, Wolinski F, Paliouras G, Karkaletsis V, Spyropoulos CD. Using machine learning to maintain rule-based named-entity recognition and classification systems. In: Proceedings of the 39th annual meeting of the association for computational linguistics; 2001. pp. 426\u201333. https:\/\/doi.org\/10.3115\/1073012.1073067.","DOI":"10.3115\/1073012.1073067"},{"key":"400_CR15","doi-asserted-by":"publisher","unstructured":"Kim JH, Woodland PC. A rule-based named entity recognition system for speech input. In: INTERSPEECH; 2000. pp. 528\u201331. https:\/\/doi.org\/10.21437\/ICSLP.2000-131.","DOI":"10.21437\/ICSLP.2000-131"},{"key":"400_CR16","unstructured":"Weegar R, Casillas A, de\u00a0Ilarraza AD, Oronoz M, P\u00e9rez A, Gojenola K. The impact of simple feature engineering in multilingual medical NER. In: Proceedings of the clinical natural language processing workshop (ClinicalNLP); 2016. pp. 1\u20136."},{"key":"400_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.cogr.2021.11.002","volume":"2","author":"R Zhang","year":"2022","unstructured":"Zhang R, Zhao P, Guo W, Wang R, Lu W. Medical named entity recognition based on dilated convolutional neural network. Cognit Robot. 2022;2:13\u201320. https:\/\/doi.org\/10.1016\/j.cogr.2021.11.002.","journal-title":"Cognit. Robot."},{"key":"400_CR18","doi-asserted-by":"publisher","unstructured":"Panchendrarajan R, Amaresan A. Bidirectional LSTM-CRF for named entity recognition. In: 14th international conference on natural computation, fuzzy systems and knowledge discovery. 32nd Pacific Asia conference on language, information and computation. IEEE; 2018. pp. 239\u201342. https:\/\/doi.org\/10.1109\/FSKD.2018.8687117.","DOI":"10.1109\/FSKD.2018.8687117"},{"key":"400_CR19","unstructured":"Lafferty JD, McCallum A, Pereira FC. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning; 2001. pp. 282\u20139."},{"issue":"4","key":"400_CR20","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682.","journal-title":"Bioinformatics"},{"issue":"1","key":"400_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthc. 2021;3(1):1\u201323. https:\/\/doi.org\/10.1145\/3458754.","journal-title":"ACM Trans Comput Healthc"},{"key":"400_CR22","doi-asserted-by":"publisher","unstructured":"Yasunaga M, Leskovec J, Liang P. LinkBERT: pretraining language models with document links. In: Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers); 2022. pp. 8003\u201316. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.551.","DOI":"10.18653\/v1\/2022.acl-long.551"},{"issue":"20","key":"400_CR23","doi-asserted-by":"publisher","first-page":"4837","DOI":"10.1093\/bioinformatics\/btac598","volume":"38","author":"M Sung","year":"2022","unstructured":"Sung M, Jeong M, Choi Y, Kim D, Lee J, Kang J. BERN2: an advanced neural biomedical named entity recognition and normalization tool. Bioinformatics. 2022;38(20):4837\u20139. https:\/\/doi.org\/10.1093\/bioinformatics\/btac598.","journal-title":"Bioinformatics"},{"issue":"12","key":"400_CR24","doi-asserted-by":"publisher","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Yang Q. A survey on multi-task learning. IEEE Trans Knowl Data Eng. 2021;34(12):5586\u2013609. https:\/\/doi.org\/10.1109\/TKDE.2021.3070203.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"400_CR25","doi-asserted-by":"publisher","first-page":"btad310","DOI":"10.1093\/bioinformatics\/btad310","volume":"39","author":"L Luo","year":"2023","unstructured":"Luo L, Wei CH, Lai PT, Leaman R, Chen Q, Lu Z. AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning. Bioinformatics. 2023;39(5):btad310. https:\/\/doi.org\/10.1093\/bioinformatics\/btad310.","journal-title":"Bioinformatics."},{"issue":"10","key":"400_CR26","doi-asserted-by":"publisher","first-page":"btae564","DOI":"10.1093\/bioinformatics\/btae564","volume":"40","author":"M S\u00e4nger","year":"2024","unstructured":"S\u00e4nger M, Garda S, Wang XD, Weber-Genzel L, Droop P, Fuchs B, et al. HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools. Bioinformatics. 2024;40(10):btae564. https:\/\/doi.org\/10.1093\/bioinformatics\/btae564.","journal-title":"Bioinformatics."},{"key":"400_CR27","doi-asserted-by":"publisher","unstructured":"Bian J, Jiang R, Zhai W, Huang T, Huang X, Zhou H, et\u00a0al. DMNER: biomedical named entity recognition by detection and matching. In: 2024 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE; 2024. pp. 872\u20138. https:\/\/doi.org\/10.1109\/BIBM62325.2024.10822274.","DOI":"10.1109\/BIBM62325.2024.10822274"},{"key":"400_CR28","doi-asserted-by":"publisher","unstructured":"Bian J, Zheng J, Zhang Y, Zhou H, Zhu S. One-shot biomedical named entity recognition via knowledge-inspired large language model. In: Proceedings of the 15th ACM international conference on bioinformatics, computational biology and health informatics; 2024. pp. 1\u201310. https:\/\/doi.org\/10.1145\/3698587.3701356.","DOI":"10.1145\/3698587.3701356"},{"key":"400_CR29","doi-asserted-by":"publisher","unstructured":"Miranda-Escalada A, Gasc\u00f3 L, Lima-L\u00f3pez S, Farr\u00e9-Maduell E, Estrada D, Nentidis A, et\u00a0al. Overview of DisTEMIST at BioASQ: automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. In: CLEF (Working Notes), vol. 3180. 2022. pp. 179\u2013203. https:\/\/doi.org\/10.5281\/zenodo.6408476.","DOI":"10.5281\/zenodo.6408476"},{"issue":"18","key":"400_CR30","doi-asserted-by":"publisher","first-page":"4449","DOI":"10.1093\/bioinformatics\/btac537","volume":"38","author":"CH Wei","year":"2022","unstructured":"Wei CH, Allot A, Riehle K, Milosavljevic A, Lu Z. tmVar 3.0: an improved variant concept recognition and normalization tool. Bioinformatics. 2022;38(18):4449\u201351. https:\/\/doi.org\/10.1093\/bioinformatics\/btac537.","journal-title":"Bioinformatics."},{"issue":"5","key":"400_CR31","doi-asserted-by":"publisher","first-page":"bbac282","DOI":"10.1093\/bib\/bbac282","volume":"23","author":"L Luo","year":"2022","unstructured":"Luo L, Lai PT, Wei CH, Arighi CN, Lu Z. BioRED: a rich biomedical relation extraction dataset. Brief Bioinform. 2022;23(5):bbac282. https:\/\/doi.org\/10.1093\/bib\/bbac282.","journal-title":"Brief Bioinform."},{"key":"400_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103779","volume":"118","author":"R Islamaj","year":"2021","unstructured":"Islamaj R, Wei CH, Cissel D, Miliaras N, Printseva O, Rodionov O, et al. NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition. J Biomed Inform. 2021;118:103779. https:\/\/doi.org\/10.1016\/j.jbi.2021.103779.","journal-title":"J Biomed Inform"},{"issue":"1","key":"400_CR33","doi-asserted-by":"publisher","first-page":"91","DOI":"10.6084\/m9.figshare.13486839","volume":"8","author":"R Islamaj","year":"2021","unstructured":"Islamaj R, Leaman R, Kim S, Kwon D, Wei CH, Comeau DC, et al. NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature. Scientific data. 2021;8(1):91. https:\/\/doi.org\/10.6084\/m9.figshare.13486839.","journal-title":"Scientific data"},{"key":"400_CR34","unstructured":"Miranda A, Mehryary F, Luoma J, Pyysalo S, Valencia A, Krallinger M. Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene\/protein relations. In: Proceedings of the seventh BioCreative challenge evaluation workshop; 2021. p. 11\u201321"},{"key":"400_CR35","doi-asserted-by":"publisher","unstructured":"Mohan S, Li D. MedMentions: a large biomedical corpus annotated with UMLS concepts. In: 1st Conference on automated knowledge base construction, AKBC 2019, Amherst, MA, USA, May 20-22, 2019; 2019. https:\/\/doi.org\/10.24432\/C5G59C.","DOI":"10.24432\/C5G59C"},{"issue":"8","key":"400_CR36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0221582","volume":"14","author":"B Kim","year":"2019","unstructured":"Kim B, Choi W, Lee H. A corpus of plant-disease relations in the biomedical domain. PLoS ONE. 2019;14(8):e0221582. https:\/\/doi.org\/10.1371\/journal.pone.0221582.","journal-title":"PLoS ONE"},{"key":"400_CR37","unstructured":"Shardlow M, Nguyen N, Owen G, O\u2019Donovan C, Leach A, McNaught J, et\u00a0al. A new corpus to support text mining for the curation of metabolites in the ChEBI database. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018); 2018"},{"key":"400_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-018-0290-y","volume":"10","author":"P Thompson","year":"2018","unstructured":"Thompson P, Daikou S, Ueno K, Batista-Navarro R, Tsujii J, Ananiadou S. Annotation and detection of drug effects in text for pharmacovigilance. J Cheminform. 2018;10:1\u201333. https:\/\/doi.org\/10.1186\/s13321-018-0290-y.","journal-title":"J Cheminform"},{"key":"400_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13326-017-0116-2","volume":"8","author":"B Bokharaeian","year":"2017","unstructured":"Bokharaeian B, Diaz A, Taghizadeh N, Chitsaz H, Chavoshinejad R. SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature. J Biomed Semant. 2017;8:1\u201313. https:\/\/doi.org\/10.1186\/s13326-017-0116-2.","journal-title":"J Biomed Semant"},{"key":"400_CR40","unstructured":"Arighi C, Hirschman L, Lemberger T, Bayer S, Liechti R, Comeau D, et\u00a0al. Bio-ID track overview. In: BioCreative VI challenge evaluation workshop. 2017, p. 482"},{"key":"400_CR41","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baw068","author":"J Li","year":"2016","unstructured":"Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database. 2016. https:\/\/doi.org\/10.1093\/database\/baw068.","journal-title":"Database."},{"issue":"18","key":"400_CR42","doi-asserted-by":"publisher","first-page":"2883","DOI":"10.1093\/bioinformatics\/btw234","volume":"32","author":"P Thomas","year":"2016","unstructured":"Thomas P, Rockt\u00e4schel T, Hakenberg J, Lichtblau Y, Leser U. SETH detects and normalizes genetic variants in text. Bioinformatics. 2016;32(18):2883\u20135. https:\/\/doi.org\/10.1093\/bioinformatics\/btw234.","journal-title":"Bioinformatics"},{"issue":"2","key":"400_CR43","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1093\/bioinformatics\/btv570","volume":"32","author":"S Kaewphan","year":"2016","unstructured":"Kaewphan S, Van Landeghem S, Ohta T, Van de Peer Y, Ginter F, Pyysalo S. Cell line name recognition in support of the identification of synthetic lethality in cancer from text. Bioinformatics. 2016;32(2):276\u201382. https:\/\/doi.org\/10.1093\/bioinformatics\/btv570.","journal-title":"Bioinformatics"},{"issue":"1","key":"400_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/918710","volume":"2015","author":"CH Wei","year":"2015","unstructured":"Wei CH, Kao HY, Lu Z. GNormPlus: an integrative approach for tagging genes, gene families, and protein domains. Biomed Res Int. 2015;2015(1):918710. https:\/\/doi.org\/10.1155\/2015\/918710.","journal-title":"Biomed Res Int"},{"key":"400_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-7-S1-S2","volume":"7","author":"M Krallinger","year":"2015","unstructured":"Krallinger M, Rabal O, Leitner F, Vazquez M, Salgado D, Lu Z, et al. The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform. 2015;7:1\u201317. https:\/\/doi.org\/10.1186\/1758-2946-7-S1-S2.","journal-title":"J Cheminform"},{"key":"400_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan RI, Leaman R, Lu Z. NCBI disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inform. 2014;47:1\u201310. https:\/\/doi.org\/10.1016\/j.jbi.2013.12.006.","journal-title":"J Biomed Inform"},{"key":"400_CR47","doi-asserted-by":"publisher","unstructured":"Bagewadi S, Bobi\u0107 T, Hofmann-Apitius M, Fluck J, Klinger R. Detecting miRNA mentions and relations in biomedical literature. F1000Research. 2015;3:205. https:\/\/doi.org\/10.12688\/f1000research.4591.3.","DOI":"10.12688\/f1000research.4591.3"},{"issue":"6","key":"400_CR48","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1093\/bioinformatics\/btt580","volume":"30","author":"S Pyysalo","year":"2014","unstructured":"Pyysalo S, Ananiadou S. Anatomical entity mention recognition at literature scale. Bioinformatics. 2014;30(6):868\u201375. https:\/\/doi.org\/10.1093\/bioinformatics\/btt580.","journal-title":"Bioinformatics"},{"key":"400_CR49","unstructured":"Kim JD, Wang Y, Yasunori Y. The Genia event extraction shared task, 2013 edition-overview. In: Proceedings of the BioNLP shared task 2013 workshop; 2013. pp. 8\u201315."},{"issue":"6","key":"400_CR50","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0065390","volume":"8","author":"E Pafilis","year":"2013","unstructured":"Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, et al. The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text. PLoS ONE. 2013;8(6):e65390. https:\/\/doi.org\/10.1371\/journal.pone.0065390.","journal-title":"PLoS ONE"},{"key":"400_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-16-S10-S2","volume":"16","author":"S Pyysalo","year":"2015","unstructured":"Pyysalo S, Ohta T, Rak R, Rowley A, Chun HW, Jung SJ, et al. Overview of the cancer genetics and pathway curation tasks of BioNLP shared task 2013. BMC Bioinform. 2015;16:1\u201319. https:\/\/doi.org\/10.1186\/1471-2105-16-S10-S2.","journal-title":"BMC Bioinform"},{"issue":"5","key":"400_CR52","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","volume":"46","author":"M Herrero-Zazo","year":"2013","unstructured":"Herrero-Zazo M, Segura-Bedmar I, Mart\u00ednez P, Declerck T. The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J Biomed Inform. 2013;46(5):914\u201320. https:\/\/doi.org\/10.1016\/j.jbi.2013.07.011.","journal-title":"J Biomed Inform"},{"key":"400_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-13-161","volume":"13","author":"M Bada","year":"2012","unstructured":"Bada M, Eckert M, Evans D, Garcia K, Shipley K, Sitnikov D, et al. Concept annotation in the CRAFT corpus. BMC Bioinform. 2012;13:1\u201320. https:\/\/doi.org\/10.1186\/1471-2105-13-161.","journal-title":"BMC Bioinform"},{"key":"400_CR54","unstructured":"Neves M, Damaschun A, Kurtz A, Leser U. Annotating and evaluating text for stem cell research. In: Proceedings of the third workshop on building and evaluation resources for biomedical text mining (BioTxtM 2012) at language resources and evaluation (LREC). Istanbul, Turkey; 2012. pp. 16\u201323."},{"key":"400_CR55","doi-asserted-by":"publisher","unstructured":"Pyysalo S, Ohta T, Rak R, Sullivan D, Mao C, Wang C, et\u00a0al. Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011. In: BMC bioinformatics. vol.\u00a013. Springer, New York; 2012. pp. 1\u201326. https:\/\/doi.org\/10.1186\/1471-2105-13-S11-S2.","DOI":"10.1186\/1471-2105-13-S11-S2"},{"issue":"5","key":"400_CR56","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1093\/bioinformatics\/btq002","volume":"26","author":"X Wang","year":"2010","unstructured":"Wang X, Tsujii J, Ananiadou S. Disambiguating the species of biomedical named entities using natural language parsers. Bioinformatics. 2010;26(5):661\u20137. https:\/\/doi.org\/10.1093\/bioinformatics\/btq002.","journal-title":"Bioinformatics"},{"key":"400_CR57","unstructured":"Gurulingappa H, Klinger R, Hofmann-Apitius M, Fluck J. An empirical evaluation of resources for the identification of diseases and adverse effects in biomedical literature. In: 2nd Workshop on Building and evaluating resources for biomedical text mining (7th edition of the language resources and evaluation conference); 2010. pp. 15\u201322"},{"key":"400_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-11-85","volume":"11","author":"M Gerner","year":"2010","unstructured":"Gerner M, Nenadic G, Bergman CM. LINNAEUS: a species name identification system for biomedical literature. BMC Bioinform. 2010;11:1\u201317. https:\/\/doi.org\/10.1186\/1471-2105-11-85.","journal-title":"BMC Bioinform"},{"key":"400_CR59","unstructured":"Hahn U, Tomanek K, Beisswanger E, Faessler E. A proposal for a configurable silver standard. In: Proceedings of the fourth linguistic annotation workshop; 2010. pp. 235\u201342."},{"key":"400_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2008-9-s2-s2","volume":"9","author":"L Smith","year":"2008","unstructured":"Smith L, Tanabe LK, Ando RJN, Kuo CJ, Chung IF, Hsu CN, et al. Overview of BioCreative II gene mention recognition. Genome Biol. 2008;9:1\u201319. https:\/\/doi.org\/10.1186\/gb-2008-9-s2-s2.","journal-title":"Genome Biol"},{"key":"400_CR61","unstructured":"Kol\u00e1rik C, Klinger R, Friedrich CM, Hofmann-Apitius M, Fluck J. Chemical names: terminological resources and corpora annotation. In: Workshop on building and evaluating resources for biomedical text mining (6th edition of the language resources and evaluation conference), vol. 36. 2008. pp. 51\u201358."},{"key":"400_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-9-84","volume":"9","author":"LI Furlong","year":"2008","unstructured":"Furlong LI, Dach H, Hofmann-Apitius M, Sanz F. OSIRISv1. 2: a named entity recognition system for sequence variants of genes in biomedical literature. BMC Bioinform. 2008;9:1\u201316. https:\/\/doi.org\/10.1186\/1471-2105-9-84.","journal-title":"BMC Bioinform"},{"key":"400_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-8-50","volume":"8","author":"S Pyysalo","year":"2007","unstructured":"Pyysalo S, Ginter F, Heimonen J, Bj\u00f6rne J, Boberg J, J\u00e4rvinen J, et al. BioInfer: a corpus for information extraction in the biomedical domain. BMC Bioinform. 2007;8:1\u201324. https:\/\/doi.org\/10.1186\/1471-2105-8-50.","journal-title":"BMC Bioinform"},{"key":"400_CR64","unstructured":"Collier N, Ohta T, Tsuruoka Y, Tateisi Y, Kim JD. Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications (NLPBA\/BioNLP); 2004. pp. 73\u20138."},{"key":"400_CR65","doi-asserted-by":"publisher","unstructured":"Ding J, Berleant D, Nettleton D, Wurtele E. Mining MEDLINE: abstracts, sentences, or phrases? In: Biocomputing 2002; 2001. pp. 326\u201337. https:\/\/doi.org\/10.1142\/9789812799623_0031.","DOI":"10.1142\/9789812799623_0031"},{"issue":"2","key":"400_CR66","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, et al. Lora: low-rank adaptation of large language models. ICLR. 2022;1(2):3.","journal-title":"ICLR"},{"key":"400_CR67","doi-asserted-by":"publisher","unstructured":"Neumann M, King D, Beltagy I, Ammar W. ScispaCy: fast and robust models for biomedical natural language processing. In: Proceedings of the 18th BioNLP workshop and shared task; 2019. pp. 319\u201327. https:\/\/doi.org\/10.18653\/v1\/W19-5034.","DOI":"10.18653\/v1\/W19-5034"},{"issue":"1","key":"400_CR68","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1093\/bioinformatics\/btz504","volume":"36","author":"JM Giorgi","year":"2020","unstructured":"Giorgi JM, Bader GD. Towards reliable named entity recognition in the biomedical domain. Bioinformatics. 2020;36(1):280\u20136. https:\/\/doi.org\/10.1093\/bioinformatics\/btz504.","journal-title":"Bioinformatics"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00400-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-025-00400-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00400-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:33:10Z","timestamp":1763746390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-025-00400-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["400"],"URL":"https:\/\/doi.org\/10.1007\/s13755-025-00400-3","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,21]]},"assertion":[{"value":"31 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"4"}}