{"id":"https://openalex.org/W3008097237","doi":"https://doi.org/10.1109/bigdata47090.2019.9006473","title":"Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features","display_name":"Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008097237","doi":"https://doi.org/10.1109/bigdata47090.2019.9006473","mag":"3008097237","pmid":"https://pubmed.ncbi.nlm.nih.gov/32455423"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101047343","display_name":"Wenli Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenli Sun","raw_affiliation_strings":["Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021164890","display_name":"Changgee Chang","orcid":"https://orcid.org/0000-0003-3426-1295"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Changgee Chang","raw_affiliation_strings":["Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002149616","display_name":"Qi Long","orcid":"https://orcid.org/0000-0003-0660-5230"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qi Long","raw_affiliation_strings":["Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Epidemiology and Informatics The University of Pennsylvania, Philadelphia, PA, 19104","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Biostatistics, The University of Pennsylvania, Philadelphia, PA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0855,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.49737955,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2019","issue":null,"first_page":"4874","last_page":"4882"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.993399977684021,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9918000102043152,"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/support-vector-machine","display_name":"Support vector machine","score":0.7608931064605713},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7437202334403992},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6734402179718018},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.581493616104126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5489252209663391},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.45063748955726624},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44871217012405396},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4356473386287689},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4317921996116638},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.42526137828826904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4241340756416321}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7608931064605713},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7437202334403992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6734402179718018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.581493616104126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5489252209663391},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.45063748955726624},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44871217012405396},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4356473386287689},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4317921996116638},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.42526137828826904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4241340756416321},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmid:32455423","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32455423","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:7243270","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243270","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Conf Big Data","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7243270","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7243270","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Conf Big Data","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.800000011920929,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1403651065","display_name":null,"funder_award_id":"P30 CA016520","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G6559044997","display_name":null,"funder_award_id":"R01 GM124111","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G8443318295","display_name":null,"funder_award_id":"RF1 AG063481","funder_id":"https://openalex.org/F4320337337","funder_display_name":"National Institute on Aging"}],"funders":[{"id":"https://openalex.org/F4320337337","display_name":"National Institute on Aging","ror":"https://ror.org/049v75w11"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W144800816","https://openalex.org/W1484418241","https://openalex.org/W1531138673","https://openalex.org/W1552425479","https://openalex.org/W1635526310","https://openalex.org/W1749992802","https://openalex.org/W1968486675","https://openalex.org/W1982992776","https://openalex.org/W2017339397","https://openalex.org/W2036183522","https://openalex.org/W2056440286","https://openalex.org/W2059503458","https://openalex.org/W2074682976","https://openalex.org/W2097839764","https://openalex.org/W2098337454","https://openalex.org/W2109526276","https://openalex.org/W2116063398","https://openalex.org/W2117526678","https://openalex.org/W2124649657","https://openalex.org/W2130139955","https://openalex.org/W2130698119","https://openalex.org/W2135046866","https://openalex.org/W2148603752","https://openalex.org/W2161148924","https://openalex.org/W2161289668","https://openalex.org/W2161347558","https://openalex.org/W2167187892","https://openalex.org/W2171541130","https://openalex.org/W2310653300","https://openalex.org/W2516228874","https://openalex.org/W2791552546","https://openalex.org/W2913319647","https://openalex.org/W2963218881","https://openalex.org/W6605830872","https://openalex.org/W6636627454","https://openalex.org/W6637938255","https://openalex.org/W6674582357","https://openalex.org/W6676680159","https://openalex.org/W6679209496","https://openalex.org/W6679676526","https://openalex.org/W6698545724"],"related_works":["https://openalex.org/W2077878098","https://openalex.org/W1506744765","https://openalex.org/W2944091050","https://openalex.org/W2998817056","https://openalex.org/W2904258669","https://openalex.org/W4313815718","https://openalex.org/W996380913","https://openalex.org/W2024084279","https://openalex.org/W2920185967","https://openalex.org/W2921837939"],"abstract_inverted_index":{"Support":[0],"vector":[1],"machine":[2],"(SVM)":[3],"is":[4,153,163],"a":[5,20,100,195,203],"popular":[6],"classification":[7],"method":[8,162,177,201],"for":[9,48,107,155,208],"analysis":[10,72,108],"of":[11,22,73,79,109,143,160,180,191],"high":[12],"dimensional":[13],"data":[14,75,193],"such":[15,63],"as":[16,64],"genomics":[17],"data.":[18,111],"Recently":[19],"number":[21],"linear":[23,42,170],"SVM":[24,105,171,176],"methods":[25],"have":[26],"been":[27],"developed":[28,154],"to":[29,124,138],"achieve":[30,129],"feature":[31,84,126,131,183],"selection":[32,184],"through":[33],"either":[34],"frequentist":[35],"regularization":[36],"or":[37],"Bayesian":[38,104],"shrinkage,":[39],"but":[40],"the":[41,70,119,122,139,144,147,173,218],"assumption":[43],"may":[44],"not":[45],"be":[46,91],"plausible":[47],"many":[49],"real":[50],"applications.":[51],"In":[52,95],"addition,":[53],"recent":[54],"work":[55],"has":[56],"demonstrated":[57],"that":[58,117,199],"incorporating":[59],"known":[60],"biological":[61,87],"knowledge,":[62],"those":[65],"from":[66,194],"functional":[67],"genomics,":[68],"into":[69],"statistical":[71],"genomic":[74,192],"offers":[76],"great":[77],"promise":[78],"improved":[80],"predictive":[81],"accuracy":[82],"and":[83,165,172,182,211],"selection.":[85,127],"Such":[86],"knowledge":[88],"can":[89],"often":[90],"represented":[92],"by":[93],"graphs.":[94],"this":[96],"article,":[97],"we":[98,133],"propose":[99],"novel":[101],"knowledge-guided":[102,130],"nonlinear":[103],"approach":[106],"high-dimensional":[110],"Our":[112],"model":[113,207],"uses":[114],"graph":[115],"information":[116],"represents":[118],"relationship":[120],"among":[121],"features":[123,145],"guide":[125],"To":[128],"selection,":[132],"assign":[134],"an":[135],"Ising":[136],"prior":[137],"indicators":[140],"representing":[141],"inclusion/exclusion":[142],"in":[146,178,185],"model.":[148],"An":[149],"efficient":[150],"MCMC":[151],"algorithm":[152],"posterior":[156],"inference.":[157],"The":[158],"performance":[159],"our":[161,200],"evaluated":[164],"compared":[166],"with":[167],"several":[168],"penalized":[169],"standard":[174],"kernel":[175],"terms":[179],"prediction":[181,206],"extensive":[186],"simulation":[187],"studies.":[188],"Also,":[189],"analyses":[190],"cancer":[196],"study":[197],"show":[198],"yields":[202],"more":[204,214],"accurate":[205],"patient":[209],"survival":[210],"reveals":[212],"biologically":[213],"meaningful":[215],"results":[216],"than":[217],"existing":[219],"methods.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}
