{"id":"https://openalex.org/W3042836846","doi":"https://doi.org/10.5220/0009892601030110","title":"Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets","display_name":"Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3042836846","doi":"https://doi.org/10.5220/0009892601030110","mag":"3042836846"},"language":"en","primary_location":{"id":"doi:10.5220/0009892601030110","doi":"https://doi.org/10.5220/0009892601030110","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0009892601030110","pdf_url":null,"source":{"id":"https://openalex.org/S7407106815","display_name":"International Conference on Deep Learning Theory and Applications (DeLTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Deep Learning Theory and Applications","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.5220/0009892601030110","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076460126","display_name":"Derek J. Reiman","orcid":"https://orcid.org/0000-0002-7955-3980","observed_orcids":["https://orcid.org/0000-0002-7955-3980"]},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Derek Reiman","raw_affiliation_strings":["Department of Bioengineering, University of Illinois at Chicago 851 S Morgan St, Chicago, IL 60607","Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St., Chicago, IL 60607, U.S.A., --- Select a Country ---"],"raw_orcid":"https://orcid.org/0000-0002-7955-3980","affiliations":[{"raw_affiliation_string":"Department of Bioengineering, University of Illinois at Chicago 851 S Morgan St, Chicago, IL 60607","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St., Chicago, IL 60607, U.S.A., --- Select a Country ---","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025440962","display_name":"Yang David Dai","orcid":"https://orcid.org/0000-0002-7638-849X","observed_orcids":["https://orcid.org/0000-0002-7638-849X"]},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Dai","raw_affiliation_strings":["Department of Bioengineering, University of Illinois at Chicago 851 S Morgan St, Chicago, IL 60607","Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St., Chicago, IL 60607, U.S.A., --- Select a Country ---"],"raw_orcid":"https://orcid.org/0000-0002-7638-849X","affiliations":[{"raw_affiliation_string":"Department of Bioengineering, University of Illinois at Chicago 851 S Morgan St, Chicago, IL 60607","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St., Chicago, IL 60607, U.S.A., --- Select a Country ---","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5025440962","https://openalex.org/A5076460126"],"corresponding_institution_ids":["https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":3.6119,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.92787485,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"103","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10066","display_name":"Gut microbiota and health","score":0.7645999789237976,"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/T10066","display_name":"Gut microbiota and health","score":0.7645999789237976,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04149999842047691,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14509","display_name":"demographic modeling and climate adaptation","score":0.019300000742077827,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/microbiome-data","display_name":"microbiome data","score":0.7630000114440918},{"id":"https://openalex.org/keywords/conditional-gan","display_name":"conditional generative adversarial networks","score":0.9810000061988831},{"id":"https://openalex.org/keywords/synthetic-data-generation","display_name":"synthetic data generation","score":0.9350000023841858},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.9279999732971191},{"id":"https://openalex.org/keywords/inflammatory-bowel-disease","display_name":"inflammatory bowel disease","score":0.902999997138977},{"id":"https://openalex.org/keywords/disease-prediction","display_name":"disease prediction","score":0.9700000286102295},{"id":"https://openalex.org/keywords/association-analysis","display_name":"association analysis","score":0.5350000262260437},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.5389999747276306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7284854054450989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6960763335227966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6553798913955688},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6366847157478333},{"id":"https://openalex.org/C143121216","wikidata":"https://www.wikidata.org/wiki/Q1330402","display_name":"Microbiome","level":2,"score":0.5864608883857727},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5700705051422119},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5179513692855835},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.44665804505348206},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37556469440460205},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.19357562065124512},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07233536243438721},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0009892601030110","doi":"https://doi.org/10.5220/0009892601030110","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0009892601030110","pdf_url":null,"source":{"id":"https://openalex.org/S7407106815","display_name":"International Conference on Deep Learning Theory and Applications (DeLTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Deep Learning Theory and Applications","raw_type":"proceedings-article"},{"id":"doi:10.1101/2020.05.18.102814","doi":"https://doi.org/10.1101/2020.05.18.102814","is_oa":true,"landing_page_url":"https://doi.org/10.1101/2020.05.18.102814","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2020/05/30/2020.05.18.102814.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.5220/0009892601030110","doi":"https://doi.org/10.5220/0009892601030110","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0009892601030110","pdf_url":null,"source":{"id":"https://openalex.org/S7407106815","display_name":"International Conference on Deep Learning Theory and Applications (DeLTA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Deep Learning Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.428600013256073,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3042836846.pdf","grobid_xml":"https://content.openalex.org/works/W3042836846.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W755741475","https://openalex.org/W1522301498","https://openalex.org/W1663623302","https://openalex.org/W1965092590","https://openalex.org/W1993784588","https://openalex.org/W2031821244","https://openalex.org/W2089468765","https://openalex.org/W2131026689","https://openalex.org/W2147618390","https://openalex.org/W2183118942","https://openalex.org/W2284521682","https://openalex.org/W2402144811","https://openalex.org/W2473355215","https://openalex.org/W2572551054","https://openalex.org/W2728822570","https://openalex.org/W2794022343","https://openalex.org/W2794749944","https://openalex.org/W2797443945","https://openalex.org/W2809598685","https://openalex.org/W2898091194","https://openalex.org/W2903406988","https://openalex.org/W2921789835","https://openalex.org/W2963034797","https://openalex.org/W2964121744","https://openalex.org/W3128912982"],"related_works":["https://openalex.org/W4394759342","https://openalex.org/W3128912982","https://openalex.org/W4414905459","https://openalex.org/W4416802371","https://openalex.org/W4415623235","https://openalex.org/W4391929726","https://openalex.org/W4413766099","https://openalex.org/W7133229582","https://openalex.org/W7160954978","https://openalex.org/W4360616794"],"abstract_inverted_index":{"Abstract":[0],"The":[1],"microbiome":[2,28,69],"of":[3,27,41,47,51,68,104,115,134,178],"the":[4,42,105,127,135,151,167],"human":[5],"body":[6],"has":[7,30],"been":[8],"shown":[9],"to":[10,32,37,63,96,121],"have":[11],"profound":[12],"effects":[13],"on":[14,24,139,150],"physiological":[15],"regulation":[16],"and":[17,44],"disease":[18,120,156,176],"pathogenesis.":[19],"However,":[20],"association":[21],"analysis":[22],"based":[23,138],"statistical":[25],"modeling":[26],"data":[29,70,137,159],"continued":[31],"be":[33],"a":[34,59,65,72,93,113,179],"challenge":[35],"due":[36],"inherent":[38],"noise,":[39],"complexity":[40],"data,":[43],"high":[45],"cost":[46],"collecting":[48],"large":[49],"number":[50],"samples.":[52],"To":[53],"address":[54],"this":[55,172],"challenge,":[56],"we":[57,111,163],"employed":[58],"deep":[60],"learning":[61,148],"framework":[62],"construct":[64],"data-driven":[66],"simulation":[67],"using":[71],"conditional":[73],"generative":[74,78,128],"adversarial":[75,79,129],"network.":[76],"Conditional":[77],"networks":[80],"train":[81],"two":[82],"models":[83,149],"against":[84],"each":[85],"other":[86],"while":[87],"leveraging":[88],"side":[89],"information":[90],"learn":[91],"from":[92],"given":[94],"dataset":[95],"compute":[97],"larger":[98],"simulated":[99],"datasets":[100],"that":[101,123,145,166],"are":[102],"representative":[103,133],"original":[106,136],"dataset.":[107],"In":[108,161],"our":[109],"study,":[110],"used":[112],"cohorts":[114],"patients":[116],"with":[117],"inflammatory":[118],"bowel":[119],"show":[122,165],"not":[124],"only":[125],"can":[126,154,174],"network":[130],"generate":[131],"samples":[132,153,169],"multiple":[140],"diversity":[141],"metrics,":[142],"but":[143],"also":[144,164],"training":[146],"machine":[147],"synthetic":[152,168],"improve":[155],"prediction":[157,177],"through":[158],"augmentation.":[160],"addition,":[162],"generated":[170],"by":[171],"cohort":[173],"boost":[175],"different":[180],"external":[181],"cohort.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-07-23T00:00:00"}
