{"id":"https://openalex.org/W4313527330","doi":"https://doi.org/10.1109/bibm55620.2022.9995516","title":"Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer","display_name":"Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313527330","doi":"https://doi.org/10.1109/bibm55620.2022.9995516"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995516","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5015279499","display_name":"Masrur Sobhan","orcid":"https://orcid.org/0000-0002-1515-4366"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Masrur Sobhan","raw_affiliation_strings":["Florida International University,Knight Foundation School of Computing and Information Sciences,Miami,Florida,USA","Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida International University,Knight Foundation School of Computing and Information Sciences,Miami,Florida,USA","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045295085","display_name":"Ananda Mohan Mondal","orcid":"https://orcid.org/0000-0002-4005-9942"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ananda Mohan Mondal","raw_affiliation_strings":["Florida International University,Knight Foundation School of Computing and Information Sciences,Miami,Florida,USA","Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida International University,Knight Foundation School of Computing and Information Sciences,Miami,Florida,USA","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, USA","institution_ids":["https://openalex.org/I19700959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I19700959"],"apc_list":null,"apc_paid":null,"fwci":2.6544,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.94986571,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3152","last_page":"3159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9465000033378601,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9395999908447266,"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/lung-cancer","display_name":"Lung cancer","score":0.7370809316635132},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.5503702163696289},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5462450981140137},{"id":"https://openalex.org/keywords/oncology","display_name":"Oncology","score":0.4908767342567444},{"id":"https://openalex.org/keywords/adenocarcinoma","display_name":"Adenocarcinoma","score":0.47652706503868103},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.44008195400238037},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.43290627002716064},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.34766989946365356},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.17925366759300232}],"concepts":[{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.7370809316635132},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.5503702163696289},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5462450981140137},{"id":"https://openalex.org/C143998085","wikidata":"https://www.wikidata.org/wiki/Q162555","display_name":"Oncology","level":1,"score":0.4908767342567444},{"id":"https://openalex.org/C2781182431","wikidata":"https://www.wikidata.org/wiki/Q356033","display_name":"Adenocarcinoma","level":3,"score":0.47652706503868103},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.44008195400238037},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.43290627002716064},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.34766989946365356},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.17925366759300232},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995516","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1546952499","https://openalex.org/W1787224781","https://openalex.org/W2102140136","https://openalex.org/W2114104545","https://openalex.org/W2129888542","https://openalex.org/W2154991741","https://openalex.org/W2179438025","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2363977422","https://openalex.org/W2380817928","https://openalex.org/W2582493317","https://openalex.org/W2605409611","https://openalex.org/W2612427157","https://openalex.org/W2748959367","https://openalex.org/W2772870171","https://openalex.org/W2788403449","https://openalex.org/W2801400022","https://openalex.org/W2914323984","https://openalex.org/W2951523021","https://openalex.org/W2959399416","https://openalex.org/W2962862931","https://openalex.org/W3001414569","https://openalex.org/W3011641611","https://openalex.org/W3082700550","https://openalex.org/W3108814108","https://openalex.org/W3116286104","https://openalex.org/W3125482371","https://openalex.org/W3126970440","https://openalex.org/W3127046337","https://openalex.org/W3128809055","https://openalex.org/W3184998487","https://openalex.org/W3200602313","https://openalex.org/W3207791129","https://openalex.org/W3209572167","https://openalex.org/W6736518430","https://openalex.org/W6737947904","https://openalex.org/W6748426178","https://openalex.org/W7038994942"],"related_works":["https://openalex.org/W2533581028","https://openalex.org/W2090817358","https://openalex.org/W2111160383","https://openalex.org/W2064573700","https://openalex.org/W2056890439","https://openalex.org/W2092424613","https://openalex.org/W2967366602","https://openalex.org/W3158074726","https://openalex.org/W3133811159","https://openalex.org/W2376385980"],"abstract_inverted_index":{"Background:":[0],"Lung":[1],"cancer":[2,22,43,54,136,150],"is":[3,23,79,114],"the":[4,14,50,63,73,84,88,106,203,211,259,275,313],"leading":[5,121],"cause":[6],"of":[7,20,41,52,62,91,100,108,157,160,202,213,229],"death":[8],"compared":[9],"to":[10,72,122,133,176,284],"other":[11],"cancers":[12],"in":[13,49,95,98],"USA.":[15],"The":[16,56,299],"overall":[17],"survival":[18],"rate":[19],"lung":[21,42,53,135,149,161,163,167,174],"not":[24],"satisfactory":[25],"even":[26],"though":[27],"there":[28],"are":[29],"cutting-edge":[30],"treatment":[31],"methods":[32,65],"for":[33,111,117,148,200,205,217,236],"cancers.":[34],"Genomic":[35],"profiling":[36],"and":[37,67,102,138,166,172,181,233,241,267,269,287,309],"biomarker":[38,57,109,289],"gene":[39],"identification":[40,107],"patients":[44,113,207],"may":[45,143,306],"play":[46],"a":[47,131,282],"role":[48],"therapeutics":[51,120],"patients.":[55,151],"genes":[58,110,141,185,204,272,290,301],"identified":[59,263,302],"by":[60,291],"most":[61],"existing":[64],"(statistical":[66],"machine":[68,189,295],"learning":[69,190,296],"based)":[70],"belong":[71],"whole":[74],"cohort":[75],"or":[76],"population.":[77],"That":[78],"why":[80],"different":[81,96],"people":[82],"with":[83],"same":[85,89],"disease":[86],"get":[87],"kind":[90],"treatment,":[92],"but":[93],"results":[94,256],"outcomes":[97],"terms":[99],"success":[101],"side":[103],"effects.":[104],"So,":[105],"individual":[112,182,206],"very":[115],"crucial":[116],"finding":[118],"efficacious":[119],"precision":[123],"medicine.":[124],"Methods:":[125],"In":[126,222],"this":[127,223],"study,":[128,224],"we":[129,225],"propose":[130],"pipeline":[132,283],"identify":[134,177,285],"class-specific":[137,264],"patient-specific":[139,183,270,288,300],"key":[140,184,271],"which":[142,208,237],"help":[144],"formulate":[145],"effective":[146],"therapies":[147],"We":[152],"have":[153],"used":[154,250],"expression":[155],"profiles":[156],"two":[158,227],"types":[159],"cancers,":[162],"adenocarcinoma":[164],"(LUAD)":[165],"squamous":[168],"cell":[169],"carcinoma":[170],"(LUSC),":[171],"Healthy":[173],"tissues":[175],"LUADand":[178],"LUSC-specific":[179],"(class-specific)":[180],"using":[186,303],"an":[187,293],"explainable":[188,294],"approach,":[191],"SHaphley":[192],"Additive":[193],"ExPlanations":[194],"(SHAP).":[195],"This":[196,279],"approach":[197,261],"provides":[198],"scores":[199,305],"each":[201,214,218],"tells":[209],"us":[210],"attribution":[212],"feature":[215],"(gene)":[216],"sample":[219],"(patient).":[220],"Result:":[221],"applied":[226],"variations":[228],"SHAP-":[230],"tree":[231],"explainer":[232,235],"gradient":[234],"tree-based":[238],"classifier,":[239,244],"XGBoost,":[240],"deep":[242],"learning-based":[243],"convolutional":[245],"neural":[246],"network":[247],"(CNN)":[248],"were":[249],"as":[251],"classification":[252],"algorithms,":[253],"respectively.":[254],"Our":[255],"showed":[257],"that":[258],"proposed":[260],"successfully":[262],"(LUAD,":[265],"LUSC,":[266],"Healthy)":[268],"based":[273],"on":[274],"SHAP":[276,304],"scores.":[277],"Conclusion:":[278],"study":[280],"demonstrated":[281],"cohort-based":[286],"incorporating":[292],"technique,":[297],"SHAP.":[298],"provide":[307],"biological":[308],"clinical":[310],"insights":[311],"into":[312],"patient\u2019s":[314],"diagnosis.":[315]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
