{"id":"https://openalex.org/W7164811476","doi":"https://doi.org/10.48550/arxiv.2606.14031","title":"Applicability Condition Extraction for Therapeutic Drug-Disease Relations","display_name":"Applicability Condition Extraction for Therapeutic Drug-Disease Relations","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164811476","doi":"https://doi.org/10.48550/arxiv.2606.14031"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14031","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.14031","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138644637","display_name":"Guanting Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Guanting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005456292","display_name":"Noriki Nishida","orcid":"https://orcid.org/0000-0002-5851-7681"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nishida, Noriki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138659074","display_name":"Yuji Matsumoto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matsumoto, Yuji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138644418","display_name":"Yuki Arase","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arase, Yuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":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":0.605400025844574,"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":0.605400025844574,"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":0.17829999327659607,"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":0.09600000083446503,"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":0.6388000249862671},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6003000140190125},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4366999864578247},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3880000114440918},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.3727000057697296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355999946594238},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6388000249862671},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4909000098705292},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4316999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3917999863624573},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3880000114440918},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14031","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.14031","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7285903692245483,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Identifying":[0],"conditions":[1,41,82],"that":[2,72,112],"a":[3,10,100,109],"certain":[4],"drug":[5],"takes":[6],"therapeutic":[7,60],"effect":[8],"on":[9,28,83],"target":[11],"disease":[12],"is":[13],"crucial":[14],"for":[15,59],"clinical":[16],"decision-making":[17],"support.":[18],"However,":[19],"most":[20],"existing":[21,103],"biomedical":[22,64,84],"information":[23],"extraction":[24,58],"methods":[25],"have":[26],"focused":[27],"identifying":[29],"only":[30],"relations":[31,44,62,117],"between":[32,118],"drugs":[33,119],"and":[34,80,120],"diseases,":[35,79],"while":[36],"largely":[37],"overlooking":[38],"the":[39,53,69,97],"context-specific":[40],"where":[42],"such":[43],"can":[45],"apply.":[46],"To":[47],"address":[48],"this":[49,92],"problem,":[50],"we":[51,94,107],"introduce":[52],"task":[54],"of":[55,77,99,102],"applicability":[56,81],"condition":[57],"drug-disease":[61,89],"from":[63],"research":[65],"literature.":[66],"We":[67],"create":[68],"first":[70],"dataset":[71],"has":[73],"manually":[74],"annotated":[75],"triples":[76],"drugs,":[78],"paper":[85],"abstracts":[86],"with":[87],"1,119":[88],"pairs.":[90],"Using":[91],"dataset,":[93],"systematically":[95],"evaluate":[96],"performance":[98],"range":[101],"methods.":[104],"In":[105],"addition,":[106],"propose":[108],"new":[110],"method":[111,123],"enhances":[113],"LoRA":[114],"to":[115],"consider":[116],"diseases.":[121],"Our":[122],"consistently":[124],"outperforms":[125],"strong":[126],"baselines":[127],"across":[128],"different":[129],"evaluation":[130],"settings.":[131]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}