{"id":"https://openalex.org/W4310422512","doi":"https://doi.org/10.1080/10618600.2022.2151453","title":"Cost-based Feature Selection for Network Model Choice","display_name":"Cost-based Feature Selection for Network Model Choice","publication_year":2022,"publication_date":"2022-11-30","ids":{"openalex":"https://openalex.org/W4310422512","doi":"https://doi.org/10.1080/10618600.2022.2151453","pmid":"https://pubmed.ncbi.nlm.nih.gov/37982131"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2151453","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2151453","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10655949/pdf/nihms-1862285.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049396755","display_name":"Louis Raynal","orcid":"https://orcid.org/0000-0003-2805-3254"},"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":"Louis Raynal","raw_affiliation_strings":["Harvard T.H. Chan School of Public Health","Harvard T.H. Chan School of Public Health, Boston, MA"],"raw_orcid":"https://orcid.org/0000-0003-2805-3254","affiliations":[{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health, Boston, MA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067794169","display_name":"Till Hoffmann","orcid":"https://orcid.org/0000-0003-4403-0722"},"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":true,"raw_author_name":"Till Hoffmann","raw_affiliation_strings":["Harvard T.H. Chan School of Public Health","Harvard T.H. Chan School of Public Health, Boston, MA"],"raw_orcid":"https://orcid.org/0000-0003-4403-0722","affiliations":[{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health, Boston, MA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041695925","display_name":"Jukka\u2010Pekka Onnela","orcid":"https://orcid.org/0000-0001-6613-8668"},"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":"Jukka-Pekka Onnela","raw_affiliation_strings":["Harvard T.H. Chan School of Public Health","Harvard T.H. Chan School of Public Health, Boston, MA"],"raw_orcid":"https://orcid.org/0000-0001-6613-8668","affiliations":[{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Harvard T.H. Chan School of Public Health, Boston, MA","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067794169"],"corresponding_institution_ids":["https://openalex.org/I136199984"],"apc_list":null,"apc_paid":null,"fwci":0.2353,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.48355654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"32","issue":"3","first_page":"1109","last_page":"1118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9984999895095825,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9984999895095825,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9962999820709229,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9937000274658203,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7328665852546692},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6320818662643433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5847839117050171},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5701488256454468},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5288285613059998},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5051737427711487},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5034868121147156},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.497693806886673},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4929080009460449},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48435887694358826},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.4830504059791565},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4360600709915161},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24821284413337708},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.08521965146064758}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7328665852546692},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6320818662643433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5847839117050171},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5701488256454468},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5288285613059998},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5051737427711487},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5034868121147156},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.497693806886673},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4929080009460449},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48435887694358826},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.4830504059791565},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4360600709915161},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24821284413337708},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.08521965146064758},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/10618600.2022.2151453","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2151453","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmid:37982131","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37982131","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":"Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10655949","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10655949","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10655949/pdf/nihms-1862285.pdf","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":"J Comput Graph Stat","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:10655949","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10655949","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10655949/pdf/nihms-1862285.pdf","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":"J Comput Graph Stat","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1287892193","display_name":null,"funder_award_id":"R01 AI138901","funder_id":"https://openalex.org/F4320337355","funder_display_name":"National Institute of Allergy and Infectious Diseases"},{"id":"https://openalex.org/G1808139159","display_name":null,"funder_award_id":"#R01AI138901","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G21374144","display_name":null,"funder_award_id":"T32-MH017119","funder_id":"https://openalex.org/F4320337346","funder_display_name":"National Institute of Mental Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337346","display_name":"National Institute of Mental Health","ror":"https://ror.org/04xeg9z08"},{"id":"https://openalex.org/F4320337355","display_name":"National Institute of Allergy and Infectious Diseases","ror":"https://ror.org/043z4tv69"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4310422512.pdf","grobid_xml":"https://content.openalex.org/works/W4310422512.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W190437827","https://openalex.org/W1487321909","https://openalex.org/W1676999057","https://openalex.org/W1808644423","https://openalex.org/W1946356339","https://openalex.org/W1995875735","https://openalex.org/W2008620264","https://openalex.org/W2012933304","https://openalex.org/W2034795216","https://openalex.org/W2040884411","https://openalex.org/W2048451896","https://openalex.org/W2056168656","https://openalex.org/W2065304353","https://openalex.org/W2084668512","https://openalex.org/W2092939357","https://openalex.org/W2094779007","https://openalex.org/W2109248504","https://openalex.org/W2118561568","https://openalex.org/W2132022337","https://openalex.org/W2135046866","https://openalex.org/W2143043751","https://openalex.org/W2154053567","https://openalex.org/W2156504490","https://openalex.org/W2162833336","https://openalex.org/W2167869241","https://openalex.org/W2182515964","https://openalex.org/W2195453971","https://openalex.org/W2210387432","https://openalex.org/W2216946510","https://openalex.org/W2330820318","https://openalex.org/W2398706673","https://openalex.org/W2406742935","https://openalex.org/W2795896985","https://openalex.org/W2798398359","https://openalex.org/W2895152177","https://openalex.org/W2900667352","https://openalex.org/W2911964244","https://openalex.org/W2953877793","https://openalex.org/W2959696098","https://openalex.org/W2963218043","https://openalex.org/W2964278775","https://openalex.org/W2965604277","https://openalex.org/W3001875368","https://openalex.org/W3099839986","https://openalex.org/W3105524694","https://openalex.org/W4248606303","https://openalex.org/W4299813518"],"related_works":["https://openalex.org/W1985846739","https://openalex.org/W2146620998","https://openalex.org/W2733224243","https://openalex.org/W2170855736","https://openalex.org/W2254500280","https://openalex.org/W3123699969","https://openalex.org/W4297813618","https://openalex.org/W4294224791","https://openalex.org/W1857615231","https://openalex.org/W2951100694"],"abstract_inverted_index":{"Selecting":[0],"a":[1,8,16,134],"small":[2],"set":[3],"of":[4,11,33,52,59,86,93,105,113,136,146],"informative":[5,35],"features":[6,36,54,120],"from":[7],"large":[9],"number":[10],"possibly":[12],"noisy":[13],"candidates":[14],"is":[15,43],"challenging":[17],"problem":[18,70],"with":[19,124],"many":[20],"applications":[21],"in":[22],"machine":[23],"learning":[24],"and":[25,159],"approximate":[26],"Bayesian":[27],"computation.":[28],"In":[29],"practice,":[30],"the":[31,49,66,84,97,130,144,161],"cost":[32,85,98,132],"computing":[34],"also":[37],"needs":[38],"to":[39,81,152,171],"be":[40,100],"considered.":[41],"This":[42,127],"particularly":[44],"important":[45],"for":[46,65,83,90],"networks":[47,158],"because":[48],"computational":[50,131],"costs":[51],"individual":[53],"can":[55,99],"span":[56],"several":[57],"orders":[58,104],"magnitude.":[60],"We":[61,88],"addressed":[62],"this":[63],"issue":[64],"network":[67,94],"model":[68],"selection":[69,79],"using":[71,121],"two":[72,91,103],"approaches.":[73],"First,":[74],"we":[75,118,149],"adapted":[76],"nine":[77],"feature":[78],"methods":[80],"account":[82],"features.":[87],"show":[89],"classes":[92],"models":[95],"that":[96],"reduced":[101,129],"by":[102,133],"magnitude":[106],"without":[107,138],"considerably":[108],"affecting":[109,139],"classification":[110,140],"accuracy":[111],"(proportion":[112],"correctly":[114],"identified":[115,160],"models).":[116],"Second,":[117],"selected":[119],"pilot":[122],"simulations":[123],"smaller":[125],"networks.":[126],"approach":[128],"factor":[135],"50":[137],"accuracy.":[141],"To":[142],"demonstrate":[143],"utility":[145],"our":[147,173],"approach,":[148],"applied":[150],"it":[151],"three":[153],"different":[154],"yeast":[155],"protein":[156],"interaction":[157],"best-fitting":[162],"duplication":[163],"divergence":[164],"model.":[165],"Supplemental":[166],"materials,":[167],"including":[168],"computer":[169],"code":[170],"reproduce":[172],"results,":[174],"are":[175],"available":[176],"online.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}