{"id":"https://openalex.org/W2749792424","doi":"https://doi.org/10.1145/3107411.3110413","title":"Investigation of Model Stacking for Drug Sensitivity Prediction","display_name":"Investigation of Model Stacking for Drug Sensitivity Prediction","publication_year":2017,"publication_date":"2017-08-20","ids":{"openalex":"https://openalex.org/W2749792424","doi":"https://doi.org/10.1145/3107411.3110413","mag":"2749792424"},"language":"en","primary_location":{"id":"doi:10.1145/3107411.3110413","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3107411.3110413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","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/A5090860214","display_name":"Kevin Matlock","orcid":"https://orcid.org/0000-0003-1658-7427"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kevin Matlock","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076226564","display_name":"Carlos De Niz","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlos De Niz","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103824763","display_name":"Raziur Rahman","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raziur Rahman","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062778775","display_name":"Souparno Ghosh","orcid":"https://orcid.org/0000-0002-4953-1019"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Souparno Ghosh","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025450455","display_name":"Ranadip Pal","orcid":"https://orcid.org/0000-0002-9311-5901"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ranadip Pal","raw_affiliation_strings":["Texas Tech University, Lubbock, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5090860214"],"corresponding_institution_ids":["https://openalex.org/I12315562"],"apc_list":null,"apc_paid":null,"fwci":1.6598,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85778957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"772","last_page":"772"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9980999827384949,"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":0.9980999827384949,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9954000115394592,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9836999773979187,"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/stacking","display_name":"Stacking","score":0.956845760345459},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.7331857085227966},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.597160816192627},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5415660738945007},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48577526211738586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47135061025619507},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4558987617492676},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45554274320602417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4263398051261902},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3362974226474762},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09419357776641846},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07780539989471436},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.07537057995796204}],"concepts":[{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.956845760345459},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.7331857085227966},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597160816192627},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5415660738945007},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48577526211738586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47135061025619507},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4558987617492676},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45554274320602417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4263398051261902},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3362974226474762},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09419357776641846},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07780539989471436},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.07537057995796204},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3107411.3110413","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3107411.3110413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","raw_type":"proceedings-article"},{"id":"pmh:oai:digitalcommons.unl.edu:statisticsfacpub-1113","is_oa":false,"landing_page_url":"https://digitalcommons.unl.edu/statisticsfacpub/109","pdf_url":null,"source":{"id":"https://openalex.org/S4306513441","display_name":"Insecta mundi","issn_l":"0749-6737","issn":["0749-6737","1942-1354","1942-1362"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310316217","host_organization_name":"Center for Systematic Entomology","host_organization_lineage":["https://openalex.org/P4310316217"],"host_organization_lineage_names":["Center for Systematic Entomology"],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Department of Statistics: Faculty Publications","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.5,"id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G2523454395","display_name":null,"funder_award_id":"R01GM122084","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2035329725","https://openalex.org/W4376641153","https://openalex.org/W2070875936","https://openalex.org/W4250391473","https://openalex.org/W3045075405","https://openalex.org/W4302292679","https://openalex.org/W4241625287","https://openalex.org/W2050788868","https://openalex.org/W4295885776","https://openalex.org/W4394984040"],"abstract_inverted_index":{"A":[0,20],"requirement":[1],"for":[2,24,63,260,299],"precision":[3],"medicine":[4],"is":[5,27,55,61,154,211,273,336,364],"the":[6,10,28,47,83,105,122,204,225,232,267,293,296,306,332,346,352,356,362,368],"accurate":[7],"prediction":[8,26,45,80,120],"of":[9,12,30,49,85,93,107,152,163,256,308,348,355,359],"sensitivity":[11,44,79,109,119],"a":[13,77,91,137,143,190,247,254,342],"given":[14],"drug":[15,43,65,78,108,118,270],"on":[16,34,319,331],"an":[17],"individual":[18,133,234,261,300,309],"patient.":[19],"very":[21],"common":[22],"method":[23,140],"this":[25,99,287],"use":[29,48,84],"Random":[31,123],"Forest":[32],"built":[33,89,141,318,330],"Genomic":[35],"Features":[36],"such":[37,59],"as":[38,203,219,221],"gene":[39],"expression.":[40],"However,":[41],"effective":[42,156],"requires":[46,82],"multiple":[50,87],"heterogeneous":[51,321],"dataset":[52],"but":[53],"it":[54],"rare":[56],"that":[57,196,315,339],"all":[58,64,73],"information":[60],"available":[62,74],"and":[66,96,127,176,179,278,310],"cell":[67,262],"line":[68,302],"combinations.":[69,303],"To":[70,131],"effectively":[71],"incorporate":[72],"data":[75,222,251,272,274,288],"into":[76],"problem":[81],"stacking":[86,103,139,153,164,198,237,316,327,340],"models,":[88],"using":[90],"variety":[92,255],"different":[94,328],"methods":[95],"data.":[97],"In":[98],"article":[100],"we":[101,112,135,193,245,289],"investigate":[102,149,165],"in":[104,117,157,169,345,361],"context":[106],"prediction.":[110],"First,":[111],"examine":[113,305],"basic":[114],"models":[115,134,317,329],"utilized":[116],"including":[121],"Forest,":[124],"Neural":[125],"Network,":[126],"K-Nearest":[128],"Neighbor":[129],"approach.":[130],"stack":[132],"utilize":[136],"linear":[138],"utilizing":[142,214,250,283],"held-out":[144],"validation":[145],"set.":[146],"We":[147,304,313],"then":[148,177,212],"which":[150,170],"form":[151],"most":[155],"improving":[158],"accuracy.":[159],"The":[160],"two":[161,320],"forms":[162],"are":[166,174,185,264,281],"vertical":[167,200,236],"stacking,":[168,181,201],"our":[171,183,349],"training":[172],"samples":[173],"split":[175,186],"stacked,":[178],"horizontal":[180,197,242],"where":[182],"features":[184,280],"before":[187],"stacking.":[188,243],"From":[189],"theoretical":[191],"standpoint":[192],"have":[194],"shown":[195],"outperforms":[199],"especially":[202],"sample":[205],"size":[206],"becomes":[207],"large.":[208],"Our":[209],"theory":[210],"proven":[213],"both":[215],"synthetically":[216],"generated":[217],"data,":[218],"well":[220],"extracted":[223,252,282],"from":[224,253,266,276],"Cancer":[226],"Cell":[227],"Line":[228],"Encyclopedia.":[229],"Despite":[230],"having":[231],"best":[233],"predictor,":[235],"consistently":[238],"underperformed":[239],"compared":[240],"to":[241,291,326],"Finally,":[244],"build":[246],"stacked":[248,311],"model":[249],"sources.":[257],"Gene":[258],"Expression":[259],"lines":[263],"taken":[265],"GDSC":[268],"database,":[269],"target":[271],"mined":[275],"Pubchem":[277],"physical":[279],"PaDEL-Descriptor":[284],"software.":[285],"Using":[286],"aim":[290],"predict":[292],"Area":[294],"Under":[295],"Curve":[297],"value":[298],"drug-cell":[301],"performance":[307,325],"models.":[312],"note":[314],"datasets":[322],"provide":[323],"superior":[324],"same":[333],"dataset.":[334],"It":[335],"also":[337],"noted":[338],"provides":[341],"noticeable":[343],"reduction":[344],"bias":[347],"predictors":[350],"when":[351],"dominant":[353],"eigenvalue":[354],"principle":[357],"axis":[358],"variation":[360],"residuals":[363],"significantly":[365],"higher":[366],"than":[367],"remaining":[369],"eigenvalues.":[370]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}