{"id":"https://openalex.org/W2760983714","doi":"https://doi.org/10.1109/ccta.2017.8062583","title":"Integrating biological data across multiple platforms using importance-weighted transfer learning and applications to breast cancer data sets","display_name":"Integrating biological data across multiple platforms using importance-weighted transfer learning and applications to breast cancer data sets","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2760983714","doi":"https://doi.org/10.1109/ccta.2017.8062583","mag":"2760983714"},"language":"en","primary_location":{"id":"doi:10.1109/ccta.2017.8062583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccta.2017.8062583","pdf_url":null,"source":{"id":"https://openalex.org/S4306498159","display_name":"2017 IEEE Conference on Control Technology and Applications (CCTA)","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":"2017 IEEE Conference on Control Technology and Applications (CCTA)","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/A5057839946","display_name":"Hema Kumari Achanta","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hema K. Achanta","raw_affiliation_strings":["GE Corporate R&D, Schenectady, NY"],"affiliations":[{"raw_affiliation_string":"GE Corporate R&D, Schenectady, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041219457","display_name":"Burook Misganaw","orcid":null},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Burook Misganaw","raw_affiliation_strings":["John Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA"],"affiliations":[{"raw_affiliation_string":"John Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089027398","display_name":"M. Vidyasagar","orcid":"https://orcid.org/0000-0003-1057-1942"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Vidyasagar","raw_affiliation_strings":["Department of Systems Engineering, The University of Texas at Dallas, Richardson, TX"],"affiliations":[{"raw_affiliation_string":"Department of Systems Engineering, The University of Texas at Dallas, Richardson, TX","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057839946"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41634668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"955","last_page":"960"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987999796867371,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980000257492065,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9944999814033508,"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/weighting","display_name":"Weighting","score":0.6886898875236511},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6087934970855713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6042025685310364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5961872339248657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5753990411758423},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5154529213905334},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.49436450004577637},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.49175289273262024},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.42418789863586426}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6886898875236511},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6087934970855713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6042025685310364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961872339248657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5753990411758423},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5154529213905334},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.49436450004577637},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.49175289273262024},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.42418789863586426},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccta.2017.8062583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccta.2017.8062583","pdf_url":null,"source":{"id":"https://openalex.org/S4306498159","display_name":"2017 IEEE Conference on Control Technology and Applications (CCTA)","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":"2017 IEEE Conference on Control Technology and Applications (CCTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6100000143051147,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1749992802","https://openalex.org/W2011645162","https://openalex.org/W2034368206","https://openalex.org/W2051381803","https://openalex.org/W2103851188","https://openalex.org/W2111355007","https://openalex.org/W2120587290","https://openalex.org/W2131953535","https://openalex.org/W2147767721","https://openalex.org/W2162651021","https://openalex.org/W2165698076","https://openalex.org/W2498991919","https://openalex.org/W2734344639","https://openalex.org/W6637938255","https://openalex.org/W6675410418","https://openalex.org/W6676997247"],"related_works":["https://openalex.org/W590788508","https://openalex.org/W4235873430","https://openalex.org/W2611974471","https://openalex.org/W4313233093","https://openalex.org/W2358082531","https://openalex.org/W2589976903","https://openalex.org/W2335596023","https://openalex.org/W2026719400","https://openalex.org/W2374273535","https://openalex.org/W2350219411"],"abstract_inverted_index":{"Traditional":[0],"machine":[1],"learning":[2,22,45],"approaches":[3],"are":[4,125],"based":[5],"on":[6,37,201],"the":[7,10,59,67,74,83,97,103,118,135,144,148,161,168,180],"premise":[8],"that":[9],"training":[11,104],"and":[12],"testing":[13,98,113],"samples":[14],"come":[15],"from":[16,90],"a":[17,41,47,109],"common":[18],"probability":[19,95],"distribution.":[20],"Transfer":[21,44,77],"refers":[23],"to":[24,71,102,147,167,176],"situations":[25],"where":[26],"this":[27,55,128],"assumption":[28],"does":[29],"not":[30],"necessarily":[31],"hold.":[32],"Integrating":[33],"biological":[34,87],"data":[35,88,99,105,205],"measured":[36],"diverse":[38,91],"platforms":[39],"is":[40,46,106,160,179],"major":[42],"challenge.":[43],"natural":[48],"candidate":[49],"for":[50],"achieving":[51],"such":[52],"integration.":[53],"In":[54],"paper,":[56],"we":[57,142,171],"adapt":[58],"\u2113":[60,119,185],"<sub":[61,120,186],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[62,121,187],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[63,122,188],"-norm":[64,123,189],"SVM":[65,124,190],"using":[66,108,127,199],"importance":[68,136,193],"weighting":[69,194],"approach":[70],"fit":[72],"into":[73],"paradigm":[75],"of":[76,85,96,112,117,150],"Learning":[78],"under":[79],"Covariate":[80],"Shift,":[81],"with":[82,100],"aim":[84],"integrating":[86],"sources":[89],"platforms.":[92],"The":[93,115,184],"conditional":[94,130],"respect":[101],"estimated":[107,129],"small":[110],"number":[111],"samples.":[114],"weights":[116],"adapted":[126],"probability,":[131],"also":[132],"known":[133],"as":[134,155],"weight.":[137],"To":[138],"validate":[139],"our":[140],"approach,":[141],"applied":[143],"proposed":[145],"algorithm":[146],"problem":[149],"classifying":[151],"breast":[152],"cancer":[153],"tumors":[154],"ER-positive":[156],"or":[157],"ER-negative,":[158],"which":[159,178],"first":[162],"step":[163],"in":[164],"personalizing":[165],"therapy":[166],"patient.":[169],"Then":[170],"compared":[172],"it":[173],"against":[174],"conversion":[175],"Z-scores,":[177,200],"current":[181],"best":[182],"practice.":[183],"modified":[191],"via":[192],"shows":[195],"better":[196],"performance":[197],"than":[198],"five":[202],"different":[203],"test":[204],"sets.":[206]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}