{"id":"https://openalex.org/W4410359036","doi":"https://doi.org/10.1109/icoin63865.2025.10992878","title":"Deep Learning for Drug Response Prediction with Gene Expression Data","display_name":"Deep Learning for Drug Response Prediction with Gene Expression Data","publication_year":2025,"publication_date":"2025-01-15","ids":{"openalex":"https://openalex.org/W4410359036","doi":"https://doi.org/10.1109/icoin63865.2025.10992878"},"language":"en","primary_location":{"id":"doi:10.1109/icoin63865.2025.10992878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin63865.2025.10992878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Information Networking (ICOIN)","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/A5104037678","display_name":"Sardar Ali","orcid":"https://orcid.org/0009-0006-7938-882X"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sardar Jaffar Ali","raw_affiliation_strings":["Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068546293","display_name":"Muhammad Omer","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Omer","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Computer Science and Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Computer Science and Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041230893","display_name":"Duc-Tai Le","orcid":"https://orcid.org/0000-0002-5286-6629"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Duc Tai Le","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022697983","display_name":"Syed M. Raza","orcid":"https://orcid.org/0000-0001-6580-3232"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Syed M. Raza","raw_affiliation_strings":["University of Glasgow,James Watt School of Engineering,Glasgow,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Glasgow,James Watt School of Engineering,Glasgow,United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080942297","display_name":"Hyunseung Choo","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunseung Choo","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.6776,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92704965,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"632","last_page":"635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.996999979019165,"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/T10885","display_name":"Gene expression and cancer classification","score":0.996999979019165,"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/T11970","display_name":"Molecular Biology Techniques and Applications","score":0.9697999954223633,"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":0.9627000093460083,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.564884603023529},{"id":"https://openalex.org/keywords/gene-expression","display_name":"Gene expression","score":0.4746844470500946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4575895071029663},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42788833379745483},{"id":"https://openalex.org/keywords/drug-response","display_name":"Drug response","score":0.41372743248939514},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.39669978618621826},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.3786289393901825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37312379479408264},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.36865049600601196},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.17796555161476135},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.16545873880386353},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.12918001413345337}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.564884603023529},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.4746844470500946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4575895071029663},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42788833379745483},{"id":"https://openalex.org/C2994119904","wikidata":"https://www.wikidata.org/wiki/Q1251001","display_name":"Drug response","level":3,"score":0.41372743248939514},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.39669978618621826},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.3786289393901825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37312379479408264},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.36865049600601196},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.17796555161476135},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.16545873880386353},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.12918001413345337}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icoin63865.2025.10992878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoin63865.2025.10992878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Information Networking (ICOIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2043398720","https://openalex.org/W2129860849","https://openalex.org/W2807463161","https://openalex.org/W3088017652","https://openalex.org/W3094540299","https://openalex.org/W3134442664","https://openalex.org/W3200428569"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4206379684","https://openalex.org/W4383066092","https://openalex.org/W4380075502","https://openalex.org/W1997438133"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1,32],"drug":[2],"responses":[3],"based":[4],"on":[5],"individual":[6],"patient":[7],"profiles":[8],"is":[9],"a":[10,27],"critical":[11,108],"challenge":[12],"in":[13,34,106,110],"personalized":[14],"medicine,":[15],"primarily":[16],"due":[17],"to":[18],"the":[19,45,51,68,78,85,98,101],"complex":[20],"biological":[21,92],"variability":[22],"involved.":[23],"This":[24],"paper":[25],"presents":[26],"deep":[28],"learning":[29],"framework":[30],"for":[31],"changes":[33],"gene":[35,111],"expression,":[36],"providing":[37],"insights":[38],"into":[39],"how":[40],"drugs":[41],"impact":[42],"cells":[43],"at":[44],"molecular":[46],"level.":[47],"Using":[48],"data":[49],"from":[50],"Kaggle":[52],"competition,":[53],"several":[54],"models":[55],"have":[56],"been":[57],"evaluated,":[58],"including":[59],"LSTM,":[60],"GRU,":[61],"Transformer,":[62],"and":[63,84],"Autoencoder":[64],"architectures.":[65],"Among":[66],"these,":[67],"3":[69],"-stacked":[70],"GRU":[71],"with":[72],"Attention":[73],"demonstrated":[74],"superior":[75],"performance,":[76],"achieving":[77],"highest":[79],"sign":[80],"accuracy":[81],"of":[82,97,103],"79%":[83],"lowest":[86],"mean":[87],"absolute":[88],"error":[89],"across":[90],"diverse":[91],"conditions.":[93],"The":[94],"robust":[95],"performance":[96],"model":[99],"highlights":[100],"effectiveness":[102],"attention":[104],"mechanisms":[105],"capturing":[107],"patterns":[109],"expression":[112],"data.":[113]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}