{"id":"https://openalex.org/W7126016412","doi":"https://doi.org/10.1109/bibm66473.2025.11356459","title":"HyperCausal: A Multi-Task Causal Learning Framework for Cancer Drug Response Prediction","display_name":"HyperCausal: A Multi-Task Causal Learning Framework for Cancer Drug Response Prediction","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126016412","doi":"https://doi.org/10.1109/bibm66473.2025.11356459"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356459","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/A5041932219","display_name":"Jiajie Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajie Xing","raw_affiliation_strings":["Inner Mongolia University,Department of Computer Science,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Department of Computer Science,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124150511","display_name":"Yuan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zhang","raw_affiliation_strings":["Inner Mongolia University,Department of Computer Science,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Department of Computer Science,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124226290","display_name":"Juan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Wang","raw_affiliation_strings":["Inner Mongolia University,Department of Computer Science,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Department of Computer Science,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2722730"],"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":"797","last_page":"802"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.4805999994277954,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.4805999994277954,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.09939999878406525,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.08309999853372574,"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/causal-inference","display_name":"Causal inference","score":0.7574999928474426},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5734000205993652},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5620999932289124},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.43470001220703125},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.4320000112056732},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.423799991607666},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.3946000039577484},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.37049999833106995}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7574999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6007000207901001},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5688999891281128},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5559999942779541},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.423799991607666},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.3946000039577484},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356459","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":[{"id":"https://metadata.un.org/sdg/3","score":0.6009089350700378,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G8735221117","display_name":null,"funder_award_id":"62571280","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"},{"id":"https://openalex.org/F4320328958","display_name":"Department of Science and Technology of Inner Mongolia","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2108068107","https://openalex.org/W2742007096","https://openalex.org/W2805406480","https://openalex.org/W2944727022","https://openalex.org/W2946834067","https://openalex.org/W2955502047","https://openalex.org/W3041880399","https://openalex.org/W3116278528","https://openalex.org/W3192677404","https://openalex.org/W3209882608","https://openalex.org/W4385296908","https://openalex.org/W4387455607","https://openalex.org/W4390339624","https://openalex.org/W4392298394","https://openalex.org/W4392962322","https://openalex.org/W4393007538","https://openalex.org/W4394964969","https://openalex.org/W4398226202","https://openalex.org/W4403845965","https://openalex.org/W4404729461","https://openalex.org/W4406949699"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,47],"of":[2,9,104,108,119],"anti-cancer":[3],"drug":[4,45,143],"response":[5,46],"requires":[6],"simultaneous":[7],"assessment":[8],"both":[10],"categorical":[11],"sensitivity":[12],"and":[13,48,56,94,106,149],"Individual":[14],"Treatment":[15],"Effects":[16],"(ITE).":[17],"Current":[18],"computational":[19,142],"approaches":[20],"predominantly":[21],"focus":[22],"on":[23,83,92,110,133],"correlations":[24],"rather":[25],"than":[26],"causal":[27,41,88,120],"mechanisms.":[28],"We":[29],"present":[30],"HyperCausal,":[31],"a":[32,138],"novel":[33],"multi-task":[34],"framework":[35,64,101],"integrating":[36],"hypergraph":[37,71],"representation":[38,80],"learning":[39],"with":[40,145],"inference":[42,121],"for":[43,86,141,154],"robust":[44,87],"personalized":[49],"treatment":[50],"selection.":[51],"HyperCausal":[52],"constructs":[53],"bipartite":[54],"graphs":[55],"hypergraphs":[57],"to":[58],"capture":[59],"complex":[60],"biological":[61],"relationships.":[62],"The":[63,100],"explicitly":[65],"models":[66],"network":[67],"inter-ference":[68],"effects":[69],"through":[70,129],"convolutional":[72],"networks.":[73],"To":[74],"address":[75],"confounding":[76],"bias,":[77],"we":[78],"employ":[79],"balancing":[81],"based":[82,132],"Wasserstein":[84],"distance":[85],"effect":[89],"estimation.":[90],"Evaluation":[91],"GDSC":[93,111],"CCLE":[95],"datasets":[96],"achieves":[97],"state-of-the-art":[98],"performance.":[99],"reaches":[102],"AUC":[103],"0.9074":[105],"AP":[107],"0.9073":[109],"dataset.":[112],"Comprehensive":[113],"ablation":[114],"studies":[115,125],"confirm":[116],"the":[117],"criticality":[118],"components.":[122],"EGFR":[123],"case":[124],"demonstrate":[126],"clinical":[127],"relevance":[128],"correct":[130],"stratification":[131],"mutation":[134],"status.":[135],"This":[136],"establishes":[137],"principled":[139],"approach":[140],"discovery":[144],"mechanistic":[146],"understanding.":[147],"Code":[148],"data":[150],"are":[151],"available":[152],"https://anonymous.4open.science/r/HyperCausal-2436":[153],"reproducibility.":[155]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}