{"id":"https://openalex.org/W4415320068","doi":"https://doi.org/10.1093/bioinformatics/btag322","title":"GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition","display_name":"GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W4415320068","doi":"https://doi.org/10.1093/bioinformatics/btag322","pmid":"https://pubmed.ncbi.nlm.nih.gov/42172599","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13259603"},"language":"en","primary_location":{"id":"doi:10.1093/bioinformatics/btag322","doi":"https://doi.org/10.1093/bioinformatics/btag322","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag322","pdf_url":null,"source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","ccf-a","cwts-core","doaj","dongbi-b","doyens","jufo-3","ki-jl-2","medline","norway-2","oarl","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1093/bioinformatics/btag322","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041023180","display_name":"Anthony Yazdani","orcid":"https://orcid.org/0000-0003-3309-6128","observed_orcids":["https://orcid.org/0000-0003-3309-6128"]},"institutions":[{"id":"https://openalex.org/I114457229","display_name":"University of Geneva","ror":"https://ror.org/01swzsf04","country_code":"CH","type":"education","lineage":["https://openalex.org/I114457229"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Anthony Yazdani","raw_affiliation_strings":["University of Geneva Department of Radiology and Medical Informatics, Faculty of Medicine, , Geneva, 1202,"],"raw_orcid":"https://orcid.org/0000-0003-3309-6128","affiliations":[{"raw_affiliation_string":"University of Geneva Department of Radiology and Medical Informatics, Faculty of Medicine, , Geneva, 1202,","institution_ids":["https://openalex.org/I114457229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102983479","display_name":"Ihor Stepanov","orcid":"https://orcid.org/0009-0003-3906-0238","observed_orcids":["https://orcid.org/0009-0003-3906-0238","https://orcid.org/0000-0002-5844-5972"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ihor Stepanov","raw_affiliation_strings":["Knowledgator Engineering , Kyiv,"],"raw_orcid":"https://orcid.org/0009-0003-3906-0238","affiliations":[{"raw_affiliation_string":"Knowledgator Engineering , Kyiv,","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042070839","display_name":"Douglas Teodoro","orcid":"https://orcid.org/0000-0001-6238-4503","observed_orcids":["https://orcid.org/0000-0001-6238-4503"]},"institutions":[{"id":"https://openalex.org/I114457229","display_name":"University of Geneva","ror":"https://ror.org/01swzsf04","country_code":"CH","type":"education","lineage":["https://openalex.org/I114457229"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Douglas Teodoro","raw_affiliation_strings":["University of Geneva Department of Radiology and Medical Informatics, Faculty of Medicine, , Geneva, 1202,"],"raw_orcid":"https://orcid.org/0000-0001-6238-4503","affiliations":[{"raw_affiliation_string":"University of Geneva Department of Radiology and Medical Informatics, Faculty of Medicine, , Geneva, 1202,","institution_ids":["https://openalex.org/I114457229"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041023180"],"corresponding_institution_ids":["https://openalex.org/I114457229"],"apc_list":{"value":3989,"currency":"USD","value_usd":3989},"apc_paid":{"value":3989,"currency":"USD","value_usd":3989},"fwci":8.1222,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.97017244,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"42","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9672999978065491,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9672999978065491,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.029500000178813934,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biomedical-named-entity-recognition","display_name":"biomedical named entity recognition","score":0.9750000238418579},{"id":"https://openalex.org/keywords/gliner","display_name":"GLiNER","score":0.8989999890327454},{"id":"https://openalex.org/keywords/zero-shot-learning","display_name":"zero-shot learning","score":0.5839999914169312},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.996999979019165},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.699999988079071},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.8939999938011169},{"id":"https://openalex.org/keywords/bi-encoder","display_name":"bi-encoder","score":0.6809999942779541},{"id":"https://openalex.org/keywords/inference-throughput","display_name":"inference throughput","score":0.4410000145435333},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.34299999475479126}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.839900016784668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8180999755859375},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.7750999927520752},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6819999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468999743461609},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.5024999976158142},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4399999976158142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4345000088214874},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3781000077724457},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C165141518","wikidata":"https://www.wikidata.org/wiki/Q4915126","display_name":"Biomedical text mining","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25040000677108765}],"mesh":[{"descriptor_ui":"D000098342","descriptor_name":"Large Language Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012984","descriptor_name":"Software","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":6,"locations":[{"id":"doi:10.1093/bioinformatics/btag322","doi":"https://doi.org/10.1093/bioinformatics/btag322","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag322","pdf_url":null,"source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","ccf-a","cwts-core","doaj","dongbi-b","doyens","jufo-3","ki-jl-2","medline","norway-2","oarl","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},{"id":"pmid:42172599","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42172599","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics (Oxford, England)","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13259603","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13259603/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"Text"},{"id":"pmh:oai:arXiv.org:2504.00676","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.00676","pdf_url":"https://arxiv.org/pdf/2504.00676","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:unige.ch:aou:unige:195460","doi":null,"is_oa":true,"landing_page_url":"https://archive-ouverte.unige.ch/unige:195460","pdf_url":"https://access.archive-ouverte.unige.ch/access/metadata/78845a34-b33a-41ec-a657-de6cb2c83c25/download","source":{"id":"https://openalex.org/S4306402259","display_name":"Archive ouverte UNIGE (University of Geneva)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I114457229","host_organization_name":"University of Geneva","host_organization_lineage":["https://openalex.org/I114457229"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bioinformatics, vol. 42, no. 6 (2026) btag322","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2504.00676","doi":"https://doi.org/10.48550/arxiv.2504.00676","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.00676","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1093/bioinformatics/btag322","doi":"https://doi.org/10.1093/bioinformatics/btag322","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag322","pdf_url":null,"source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","ccf-a","cwts-core","doaj","dongbi-b","doyens","jufo-3","ki-jl-2","medline","norway-2","oarl","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G5582534287","display_name":"AI-based Risk Assessment for Clinical Trials on Medicinal Products: A Large-Scale and Integrative Approach","funder_award_id":"10005385","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"},{"id":"https://openalex.org/F4320327931","display_name":"Innosuisse - Schweizerische Agentur f\u00fcr Innovationsf\u00f6rderung","ror":"https://ror.org/05a2bhn71"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7132846037","https://openalex.org/W4411113235","https://openalex.org/W7139633698","https://openalex.org/W4401836507","https://openalex.org/W6988125982","https://openalex.org/W4403144092","https://openalex.org/W4393094733","https://openalex.org/W4405427397","https://openalex.org/W7147082926","https://openalex.org/W4417100060"],"abstract_inverted_index":{"MOTIVATION:":[0],"Biomedical":[1],"named":[2],"entity":[3,40],"recognition":[4],"(NER)":[5],"presents":[6],"unique":[7],"challenges":[8],"due":[9],"to":[10,36,96,174],"specialized":[11],"vocabularies,":[12],"the":[13,19,62,77,117,129,137,150,154,159],"sheer":[14],"volume":[15],"of":[16,22,53,65,79],"entities,":[17],"and":[18,32,91,100,165,209],"continuous":[20],"emergence":[21],"novel":[23],"entities.":[24],"Traditional":[25],"NER":[26,82],"models,":[27],"constrained":[28],"by":[29,120],"fixed":[30],"taxonomies":[31],"human":[33],"annotations,":[34],"struggle":[35],"generalize":[37],"beyond":[38],"predefined":[39],"types.":[41],"RESULTS:":[42],"To":[43],"address":[44],"these":[45],"issues,":[46],"we":[47],"introduce":[48],"GLiNER-BioMed,":[49],"a":[50,71],"domain-adapted":[51],"suite":[52],"GLiNER":[54,88],"models":[55,68,210],"for":[56,194],"biomedicine.":[57],"Our":[58,146],"approach":[59],"first":[60],"distills":[61],"annotation":[63],"capabilities":[64],"large":[66],"language":[67],"(LLMs)":[69],"into":[70],"smaller,":[72],"more":[73],"efficient":[74],"model,":[75],"enabling":[76],"generation":[78],"high-coverage":[80],"biomedical":[81,105,187],"data.":[83],"We":[84],"subsequently":[85],"train":[86],"two":[87],"architectures,":[89],"uni-":[90],"bi-encoder,":[92],"at":[93,214],"multiple":[94],"scales":[95],"balance":[97],"computational":[98],"efficiency":[99],"performance.":[101],"Experiments":[102],"on":[103],"eight":[104],"datasets":[106],"demonstrate":[107],"that":[108,149,184],"GLiNER-BioMed":[109,152],"achieved":[110],"state-of-the-art":[111],"zero-shot":[112,156],"performance":[113],"(micro-F1":[114],"59.77%),":[115],"exceeding":[116],"strongest":[118,138,155],"baseline":[119,139],"5.96":[121],"points":[122],"(P":[123,143],"<":[124,144],".001).":[125],"In":[126],"few-shot":[127,163],"learning,":[128],"bi-encoder":[130,160],"variant":[131],"reached":[132],"70.39%":[133],"(10-shot),":[134],"consistently":[135],"outperforming":[136],"across":[140],"all":[141],"settings":[142],".05).":[145],"findings":[147],"show":[148],"uni-encoder":[151],"achieves":[153],"performance,":[157],"while":[158,198],"offers":[161],"superior":[162],"gains":[164],"substantially":[166],"higher":[167],"inference":[168],"throughput":[169],"(+39%-568%),":[170],"making":[171],"it":[172],"well-suited":[173],"annotation-limited,":[175],"latency-sensitive,":[176],"or":[177],"large-label-space":[178],"settings.":[179],"Ablation":[180],"studies":[181],"further":[182],"indicate":[183],"combining":[185],"synthetic":[186],"pre-training":[188],"with":[189],"general-domain":[190],"post-training":[191],"is":[192],"essential":[193],"capturing":[195],"domain-specific":[196],"knowledge":[197],"maintaining":[199],"precision-recall":[200],"balance.":[201],"AVAILABILITY":[202],"AND":[203],"IMPLEMENTATION:":[204],"The":[205],"source":[206],"code,":[207],"datasets,":[208],"are":[211],"publicly":[212],"available":[213],"https://github.com/ds4dh/GLiNER-biomed.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2025-10-18T00:00:00"}
