{"id":"https://openalex.org/W7126066640","doi":"https://doi.org/10.1109/bibm66473.2025.11357008","title":"MF-DocDDI: Drug Entity Multi-Feature Fusion for Document-Level Drug-Drug Interaction Relation Extraction","display_name":"MF-DocDDI: Drug Entity Multi-Feature Fusion for Document-Level Drug-Drug Interaction Relation Extraction","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126066640","doi":"https://doi.org/10.1109/bibm66473.2025.11357008"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357008","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5124206636","display_name":"Han Han","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Han","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123651114","display_name":"Mingliang Dou","orcid":null},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]},{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingliang Dou","raw_affiliation_strings":["College of Computer Science and Technology, Taiyuan University of Technology,Taiyuan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Taiyuan University of Technology,Taiyuan,China","institution_ids":["https://openalex.org/I46305995","https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jijun Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jijun Tang","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Shenzhen,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100702161","display_name":"Fei Guo","orcid":"https://orcid.org/0000-0001-8346-0798"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Guo","raw_affiliation_strings":["School of Computer Science and Engineering, Central South University,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Central South University,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"941","last_page":"946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.8478000164031982,"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":0.8478000164031982,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.07010000199079514,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10028","display_name":"Topic Modeling","score":0.05119999870657921,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6614999771118164},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6583999991416931},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6061999797821045},{"id":"https://openalex.org/keywords/drug-drug-interaction","display_name":"Drug-drug interaction","score":0.5210999846458435},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.41440001130104065},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4106000065803528},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.3977999985218048},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7182999849319458},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6614999771118164},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6583999991416931},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5720999836921692},{"id":"https://openalex.org/C2910466267","wikidata":"https://www.wikidata.org/wiki/Q718753","display_name":"Drug-drug interaction","level":3,"score":0.5210999846458435},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3862999975681305},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C55105296","wikidata":"https://www.wikidata.org/wiki/Q841382","display_name":"Interaction network","level":3,"score":0.3357999920845032},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C38764148","wikidata":"https://www.wikidata.org/wiki/Q17098245","display_name":"Interaction information","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.28200000524520874}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357008","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4878191351890564,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2134497330","display_name":null,"funder_award_id":"6250074582","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2067704478","https://openalex.org/W2119002393","https://openalex.org/W2123060905","https://openalex.org/W2166474856","https://openalex.org/W2264517602","https://openalex.org/W2765742249","https://openalex.org/W2767891136","https://openalex.org/W2772102631","https://openalex.org/W2802200505","https://openalex.org/W2807873602","https://openalex.org/W2911489562","https://openalex.org/W2952179106","https://openalex.org/W2960293113","https://openalex.org/W2964026782","https://openalex.org/W3035053871","https://openalex.org/W3093844547","https://openalex.org/W3103836967","https://openalex.org/W3139253280","https://openalex.org/W3155073135","https://openalex.org/W3186328715","https://openalex.org/W3188999884","https://openalex.org/W4212906192","https://openalex.org/W4378190790","https://openalex.org/W4379053404","https://openalex.org/W4388595306","https://openalex.org/W4389160572","https://openalex.org/W4395450002"],"related_works":[],"abstract_inverted_index":{"Drug-drug":[0],"interactions":[1],"(DDIs)":[2],"are":[3],"crucial":[4],"in":[5],"clinical":[6],"medicine,":[7],"as":[8,125],"they":[9],"can":[10,135],"lead":[11],"to":[12,26,42,77,88,97,120,138],"adverse":[13],"events.":[14],"Existing":[15],"DDI":[16,53,69,132],"extraction":[17,54],"methods":[18],"focus":[19],"on":[20,68],"sentence-level":[21],"tasks,":[22],"limiting":[23],"their":[24],"ability":[25,119],"identify":[27,121],"cross-sentence":[28,122],"DDIs.":[29],"Moreover,":[30],"the":[31,109],"only":[32,37],"document-level":[33,52,65],"method":[34],"available":[35],"considers":[36],"internal":[38,79],"drug":[39,57,80],"features,":[40],"leading":[41],"suboptimal":[43],"performance.":[44],"To":[45],"address":[46],"this,":[47],"we":[48,73,93],"propose":[49],"MF-DocDDI,":[50],"a":[51,64,84],"model":[55],"using":[56],"entity":[58],"multi-feature":[59],"fusion.":[60],"We":[61],"first":[62],"construct":[63],"dataset":[66],"based":[67],"Extraction":[70],"2013.":[71],"Then,":[72],"introduce":[74],"document-entity":[75],"embeddings":[76],"capture":[78],"features":[81,96],"and":[82,128],"employ":[83],"simplified":[85],"U-shaped":[86],"network":[87],"extract":[89],"external":[90],"features.":[91],"Finally,":[92],"integrate":[94],"these":[95],"enhance":[98],"interaction":[99,144],"modeling.":[100],"Experimental":[101],"results":[102],"show":[103],"MF-DocDDI":[104,134],"outperforms":[105],"existing":[106],"methods,":[107],"improving":[108],"F1":[110],"score":[111],"by":[112],"5.33":[113],"%.":[114],"Case":[115],"studies":[116],"confirm":[117],"its":[118],"DDIs,":[123],"such":[124],"(naloxone,":[126],"morphine)":[127],"(HEXALEN,":[129],"cisplatin).":[130],"Beyond":[131],"extraction,":[133],"be":[136],"applied":[137],"other":[139],"biomedical":[140],"tasks":[141],"like":[142],"protein-protein":[143],"(PPI)":[145],"extraction.":[146]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}
