{"id":"https://openalex.org/W4388581232","doi":"https://doi.org/10.1145/3624062.3624080","title":"Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction","display_name":"Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction","publication_year":2023,"publication_date":"2023-11-10","ids":{"openalex":"https://openalex.org/W4388581232","doi":"https://doi.org/10.1145/3624062.3624080"},"language":"en","primary_location":{"id":"doi:10.1145/3624062.3624080","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3624062.3624080","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis","raw_type":"proceedings-article"},"type":"article","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/A5047293381","display_name":"Oleksandr Narykov","orcid":"https://orcid.org/0000-0002-3336-0534"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Oleksandr Narykov","raw_affiliation_strings":["Data Science and Learning Division, Argonne National Laboratory (ANL), United States of America"],"affiliations":[{"raw_affiliation_string":"Data Science and Learning Division, Argonne National Laboratory (ANL), United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057761555","display_name":"Yitan Zhu","orcid":"https://orcid.org/0000-0001-5332-6125"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yitan Zhu","raw_affiliation_strings":["Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046675967","display_name":"Thomas Brettin","orcid":"https://orcid.org/0000-0001-9301-9760"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Brettin","raw_affiliation_strings":["Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044153234","display_name":"Yvonne A. Evrard","orcid":"https://orcid.org/0000-0002-3475-4850"},"institutions":[{"id":"https://openalex.org/I4210130649","display_name":"Frederick National Laboratory for Cancer Research","ror":"https://ror.org/03v6m3209","country_code":"US","type":"facility","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210130649","https://openalex.org/I4210140884"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yvonne Evrard","raw_affiliation_strings":["Frederick National Laboratory for Cancer Research, United States of America"],"affiliations":[{"raw_affiliation_string":"Frederick National Laboratory for Cancer Research, United States of America","institution_ids":["https://openalex.org/I4210130649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039059226","display_name":"Alexander Partin","orcid":"https://orcid.org/0000-0002-9279-9213"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Partin","raw_affiliation_strings":["Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040025864","display_name":"Maulik Shukla","orcid":"https://orcid.org/0009-0002-3396-5725"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maulik Shukla","raw_affiliation_strings":["Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029563560","display_name":"Priyanka Vasanthakumari","orcid":"https://orcid.org/0000-0003-0822-5936"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Priyanka Vasanthakumari","raw_affiliation_strings":["Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), Data Science and Learning Division, United States of America","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041200743","display_name":"James H. Doroshow","orcid":"https://orcid.org/0000-0002-4463-1790"},"institutions":[{"id":"https://openalex.org/I4210140884","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210140884"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Doroshow","raw_affiliation_strings":["National Cancer Institute (NCI), United States of America"],"affiliations":[{"raw_affiliation_string":"National Cancer Institute (NCI), United States of America","institution_ids":["https://openalex.org/I4210140884"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053682943","display_name":"Rick Stevens","orcid":"https://orcid.org/0000-0002-4268-4020"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rick Stevens","raw_affiliation_strings":["Argonne National Laboratory (ANL), United States of America"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory (ANL), United States of America","institution_ids":["https://openalex.org/I1282105669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5047293381"],"corresponding_institution_ids":["https://openalex.org/I1282105669"],"apc_list":null,"apc_paid":null,"fwci":0.4041,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66673456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"121","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"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/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9524000287055969,"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/regularization","display_name":"Regularization (linguistics)","score":0.610506534576416},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.573093593120575},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5643773078918457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5635226964950562},{"id":"https://openalex.org/keywords/drug-response","display_name":"Drug response","score":0.5547654032707214},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.506592869758606},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.4860444664955139},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46481403708457947},{"id":"https://openalex.org/keywords/cancer-drugs","display_name":"Cancer drugs","score":0.4615734815597534},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43531137704849243},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2238636612892151},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1864006221294403},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.17315271496772766},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.10002672672271729}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.610506534576416},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.573093593120575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5643773078918457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5635226964950562},{"id":"https://openalex.org/C2994119904","wikidata":"https://www.wikidata.org/wiki/Q1251001","display_name":"Drug response","level":3,"score":0.5547654032707214},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.506592869758606},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.4860444664955139},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46481403708457947},{"id":"https://openalex.org/C3020340455","wikidata":"https://www.wikidata.org/wiki/Q974135","display_name":"Cancer drugs","level":3,"score":0.4615734815597534},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43531137704849243},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2238636612892151},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1864006221294403},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17315271496772766},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.10002672672271729},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3624062.3624080","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3624062.3624080","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W4214831389"],"related_works":["https://openalex.org/W2326228278","https://openalex.org/W4254199101","https://openalex.org/W4300427796","https://openalex.org/W1670153145","https://openalex.org/W1987689323","https://openalex.org/W2051864124","https://openalex.org/W4237474809","https://openalex.org/W3023929447","https://openalex.org/W2021131614","https://openalex.org/W4283076622"],"abstract_inverted_index":{"This":[0],"work":[1,88],"studies":[2],"a":[3,29,74,90,103],"particular":[4],"setting":[5],"for":[6],"regression":[7],"problems":[8],"\u2013":[9],"tasks":[10],"with":[11],"complex":[12],"combinatorial":[13],"data":[14],"space":[15],"where":[16],"samples":[17],"can":[18],"be":[19,77],"divided":[20],"into":[21],"distinct":[22],"groups.":[23],"Anti-cancer":[24],"drug":[25,44],"response":[26,54],"prediction":[27],"is":[28,72],"perfect":[30],"example":[31],"of":[32,50,59],"this":[33,94],"setting,":[34],"in":[35,69,102],"which":[36],"each":[37],"sample":[38],"includes":[39],"cancer":[40],"biological":[41],"features":[42],"and":[43,52,61,80,85],"chemical":[45],"information.":[46],"Many":[47],"existing":[48],"works":[49,71],"pan-drug":[51],"pan-cancer":[53],"modeling":[55],"treat":[56],"different":[57],"combinations":[58],"drugs":[60,84],"cancers":[62],"as":[63],"individual":[64],"samples.":[65],"A":[66],"potential":[67],"problem":[68],"these":[70],"that":[73],"model":[75,99],"may":[76],"heavily":[78],"influenced":[79],"biased":[81],"toward":[82],"overrepresented":[83],"cancers.":[86],"Our":[87],"develops":[89],"method":[91],"to":[92],"solve":[93],"issue":[95],"by":[96],"adjusting":[97],"the":[98],"training":[100],"process":[101],"deep":[104],"learning":[105],"framework.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}