{"id":"https://openalex.org/W3070389369","doi":"https://doi.org/10.1093/bioinformatics/btaa869","title":"LBERT: Lexically-aware Transformers based Bidirectional Encoder Representation model for learning Universal Bio-Entity Relations","display_name":"LBERT: Lexically-aware Transformers based Bidirectional Encoder Representation model for learning Universal Bio-Entity Relations","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3070389369","doi":"https://doi.org/10.1093/bioinformatics/btaa869","mag":"3070389369","pmid":"https://pubmed.ncbi.nlm.nih.gov/33331647"},"language":"en","primary_location":{"id":"doi:10.1093/bioinformatics/btaa869","is_oa":false,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btaa869","pdf_url":null,"source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":false,"is_in_doaj":true,"is_core":true,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},"type":"erratum","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021745312","display_name":"Neha Warikoo","orcid":"https://orcid.org/0000-0003-4222-8970"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Neha Warikoo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018928362","display_name":"Yung\u2010Chun Chang","orcid":"https://orcid.org/0000-0002-9634-8380"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yung-Chun Chang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112054430","display_name":"Wen-Lian Hsu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen-Lian Hsu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021745312"],"corresponding_institution_ids":[],"apc_list":{"value":3618,"currency":"USD","value_usd":3618},"apc_paid":null,"fwci":0.87774276,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.69504296,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"36","issue":"22-23","first_page":"5565","last_page":"5565"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9979000091552734,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8239004611968994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6668309569358826},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.575337290763855},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5406169295310974},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5145787596702576},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5112638473510742},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.48200884461402893},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.478779673576355},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4675907790660858},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4595663845539093},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.45055416226387024},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.43697887659072876},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4299500584602356},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4136388301849365},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.25431668758392334},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.196546733379364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8239004611968994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6668309569358826},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.575337290763855},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5406169295310974},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5145787596702576},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5112638473510742},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.48200884461402893},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.478779673576355},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4675907790660858},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4595663845539093},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.45055416226387024},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.43697887659072876},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4299500584602356},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4136388301849365},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.25431668758392334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.196546733379364},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1093/bioinformatics/btaa869","is_oa":false,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btaa869","pdf_url":null,"source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":false,"is_in_doaj":true,"is_core":true,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},{"id":"pmid:33331647","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33331647","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,"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":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1538085078","https://openalex.org/W1574901103","https://openalex.org/W2052217781","https://openalex.org/W2064299012","https://openalex.org/W2067704478","https://openalex.org/W2108791029","https://openalex.org/W2118864375","https://openalex.org/W2121844933","https://openalex.org/W2136437513","https://openalex.org/W2166111585","https://openalex.org/W2170189740","https://openalex.org/W2264517602","https://openalex.org/W2465246611","https://openalex.org/W2485374661","https://openalex.org/W2512456704","https://openalex.org/W2519744449","https://openalex.org/W2560748602","https://openalex.org/W2734608416","https://openalex.org/W2766673096","https://openalex.org/W2787560479","https://openalex.org/W2809349863","https://openalex.org/W2883380892","https://openalex.org/W2883749461","https://openalex.org/W2884668708","https://openalex.org/W2896378533","https://openalex.org/W2904726360","https://openalex.org/W2911489562","https://openalex.org/W2913694513","https://openalex.org/W2949847915","https://openalex.org/W2960293113","https://openalex.org/W4252684946"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W3163146846"],"abstract_inverted_index":{"MOTIVATION":[0],"Natural":[1],"Language":[2],"Processing":[3],"techniques":[4],"are":[5,64,230],"constantly":[6],"being":[7],"advanced":[8],"to":[9,18],"accommodate":[10],"the":[11,26,35,135,196,199],"influx":[12],"of":[13,134,198],"data":[14,88,229],"as":[15,17,34],"well":[16],"provide":[19],"exhaustive":[20,137],"and":[21,116,121,161,169,202],"structured":[22],"knowledge":[23,46],"dissemination.":[24],"Within":[25],"biomedical":[27,54],"domain,":[28],"relation":[29,146,163,185],"detection":[30],"between":[31],"bio-entities":[32],"known":[33],"Bio-Entity":[36],"Relation":[37],"Extraction":[38],"(BRE)":[39],"task":[40,66],"has":[41],"a":[42,100,108,175],"critical":[43],"function":[44],"in":[45,51,90,155,181,205],"structuring.":[47],"Although":[48],"recent":[49],"advances":[50],"deep":[52,75,152],"learning-based":[53,76],"domain":[55],"embedding":[56],"have":[57,86],"improved":[58,87],"BRE":[59,102,138],"predictive":[60],"analytics,":[61],"these":[62],"works":[63],"often":[65],"selective":[67],"or":[68],"use":[69],"external":[70],"knowledge-based":[71],"pre-/post-processing.":[72],"In":[73,95],"addition,":[74],"models":[77,154],"do":[78],"not":[79],"account":[80],"for":[81,125,186],"local":[82,120,212],"syntactic":[83],"contexts,":[84],"which":[85,106,117],"representation":[89],"many":[91],"kernel":[92],"classifier-based":[93],"models.":[94],"this":[96],"study,":[97],"we":[98],"propose":[99],"universal":[101],"model,":[103,115],"i.e.":[104],"LBERT,":[105],"is":[107],"Lexically":[109],"aware":[110],"Transformer-based":[111],"Bidirectional":[112],"Encoder":[113],"Representation":[114],"explores":[118],"both":[119],"global":[122,219],"contexts":[123],"representations":[124,173],"sentence-level":[126],"classification":[127,164],"tasks.":[128],"RESULTS":[129],"This":[130],"article":[131],"presents":[132],"one":[133],"most":[136],"studies":[139,193],"ever":[140],"conducted":[141],"over":[142,179],"five":[143],"different":[144],"bio-entity":[145,184],"types.":[147],"Our":[148,191],"model":[149],"outperforms":[150],"state-of-the-art":[151],"learning":[153,210],"protein-protein":[156],"interaction":[157,160],"(PPI),":[158],"drug-drug":[159],"protein-bio-entity":[162],"tasks":[165],"by":[166,209],"0.02%,":[167],"11.2%":[168],"41.4%,":[170],"respectively.":[171],"LBERT":[172],"show":[174],"statistically":[176],"significant":[177],"improvement":[178],"BioBERT":[180],"detecting":[182],"true":[183],"large":[187],"corpora":[188],"like":[189],"PPI.":[190],"ablation":[192],"clearly":[194],"indicate":[195],"contribution":[197],"lexical":[200],"features":[201],"distance-adjusted":[203],"attention":[204],"improving":[206],"prediction":[207],"performance":[208],"additional":[211],"semantic":[213],"context":[214],"along":[215],"with":[216],"bi-directionally":[217],"learned":[218],"context.":[220],"AVAILABILITY":[221],"AND":[222],"IMPLEMENTATION":[223],"Github.":[224],"https://github.com/warikoone/LBERT.":[225],"SUPPLEMENTARY":[226],"INFORMATION":[227],"Supplementary":[228],"available":[231],"at":[232],"Bioinformatics":[233],"online.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}