{"id":"https://openalex.org/W1981353344","doi":"https://doi.org/10.1109/isbi.2013.6556585","title":"Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNP data","display_name":"Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNP data","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W1981353344","doi":"https://doi.org/10.1109/isbi.2013.6556585","mag":"1981353344"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2013.6556585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","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/A5079946688","display_name":"Hongbao Cao","orcid":"https://orcid.org/0000-0001-6591-9518"},"institutions":[{"id":"https://openalex.org/I114832834","display_name":"Tulane University","ror":"https://ror.org/04vmvtb21","country_code":"US","type":"education","lineage":["https://openalex.org/I114832834"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongbao Cao","raw_affiliation_strings":["Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA","institution_ids":["https://openalex.org/I114832834"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023726254","display_name":"Junbo Duan","orcid":"https://orcid.org/0000-0001-7170-3772"},"institutions":[{"id":"https://openalex.org/I114832834","display_name":"Tulane University","ror":"https://ror.org/04vmvtb21","country_code":"US","type":"education","lineage":["https://openalex.org/I114832834"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junbo Duan","raw_affiliation_strings":["Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA","institution_ids":["https://openalex.org/I114832834"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101635490","display_name":"Dongdong Lin","orcid":"https://orcid.org/0000-0001-5645-4798"},"institutions":[{"id":"https://openalex.org/I114832834","display_name":"Tulane University","ror":"https://ror.org/04vmvtb21","country_code":"US","type":"education","lineage":["https://openalex.org/I114832834"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongdong Lin","raw_affiliation_strings":["Biomedical Engineering Department, Tulane University, New Orleans, LA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Tulane University, New Orleans, LA, USA","institution_ids":["https://openalex.org/I114832834"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032850756","display_name":"Vince D. Calhoun","orcid":"https://orcid.org/0000-0001-9058-0747"},"institutions":[{"id":"https://openalex.org/I1334567473","display_name":"Mind Research Network","ror":"https://ror.org/032cjfs80","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1334567473"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vince Calhoun","raw_affiliation_strings":["Mind Research Network, Albuquerque, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mind Research Network, Albuquerque, NM, USA","institution_ids":["https://openalex.org/I1334567473"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339106","display_name":"Yu\u2010Ping Wang","orcid":"https://orcid.org/0000-0001-9340-5864"},"institutions":[{"id":"https://openalex.org/I114832834","display_name":"Tulane University","ror":"https://ror.org/04vmvtb21","country_code":"US","type":"education","lineage":["https://openalex.org/I114832834"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu-Ping Wang","raw_affiliation_strings":["Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Biostatisctics Department, Tulane University, New Orleans, LA, USA","institution_ids":["https://openalex.org/I114832834"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7055,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70087251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"756","last_page":"759"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10308","display_name":"Systemic Lupus Erythematosus Research","score":0.9807999730110168,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7502299547195435},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6908141374588013},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6564739942550659},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6377652287483215},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.6032863259315491},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5489537715911865},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.5168648362159729},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4571741223335266},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4445381760597229},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4282030463218689},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3213695287704468},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2927224636077881},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2631908655166626},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.13451844453811646},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.07937002182006836}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7502299547195435},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6908141374588013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6564739942550659},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6377652287483215},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.6032863259315491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5489537715911865},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.5168648362159729},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4571741223335266},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4445381760597229},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4282030463218689},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3213695287704468},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2927224636077881},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2631908655166626},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.13451844453811646},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.07937002182006836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2013.6556585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1972238714","https://openalex.org/W1985089773","https://openalex.org/W2003730746","https://openalex.org/W2013828302","https://openalex.org/W2020451388","https://openalex.org/W2021302824","https://openalex.org/W2043278359","https://openalex.org/W2064751940","https://openalex.org/W2070515441","https://openalex.org/W2097323375","https://openalex.org/W2098012923","https://openalex.org/W2099151709","https://openalex.org/W2105454037","https://openalex.org/W2115632542","https://openalex.org/W2116148865","https://openalex.org/W2116359130","https://openalex.org/W2120866479","https://openalex.org/W2132964759","https://openalex.org/W2136235822","https://openalex.org/W2139105070","https://openalex.org/W2140856955","https://openalex.org/W2154332973","https://openalex.org/W2162409952","https://openalex.org/W2168094269","https://openalex.org/W6643323384","https://openalex.org/W6646464445","https://openalex.org/W6680604016","https://openalex.org/W6684458755"],"related_works":["https://openalex.org/W3027020613","https://openalex.org/W2016533837","https://openalex.org/W3167885074","https://openalex.org/W2892386716","https://openalex.org/W1998563493","https://openalex.org/W4306164210","https://openalex.org/W4313316311","https://openalex.org/W4362608745","https://openalex.org/W2383143032","https://openalex.org/W2082728368"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,18,50,81,93,108],"novel":[3],"sparse":[4,20,109],"representation":[5],"based":[6],"variable":[7,14,27,96],"selection":[8,15,28,97],"algorithm":[9,48],"(SRVS),":[10],"which":[11,101],"improves":[12],"the":[13,47,78],"ability":[16],"of":[17,36,39,53,120],"traditional":[19],"regression":[21,110],"model":[22,111],"in":[23,64],"that":[24,103],"it":[25],"performs":[26],"at":[29],"different":[30,40],"significance":[31],"levels,":[32],"and":[33,56,92],"gives":[34],"groups":[35],"selected":[37,79],"variables":[38],"sizes.":[41],"As":[42],"an":[43],"example,":[44],"we":[45],"applied":[46],"to":[49,70],"joint":[51],"analysis":[52],"759075":[54],"SNPs":[55],"153594":[57],"functional":[58],"magnetic":[59],"resonance":[60],"imaging":[61],"(fMRI)":[62],"voxels":[63],"208":[65],"subjects":[66],"(92":[67],"cases/116":[68],"controls)":[69],"identify":[71],"biomarkers":[72],"for":[73],"schizophrenia":[74],"(SZ).":[75],"To":[76],"evaluate":[77],"biomarkers,":[80],"10-fold":[82],"cross":[83],"validation":[84],"was":[85],"performed.":[86],"The":[87],"results":[88],"between":[89],"SRVS":[90],"method":[91,98],"previously":[94],"reported":[95],"were":[99],"compared,":[100],"showed":[102],"our":[104],"method,":[105],"especially":[106],"with":[107,113],"penalized":[112],"norm,":[114],"gave":[115],"significantly":[116],"higher":[117],"classification":[118],"accuracy":[119],"discriminating":[121],"SZ":[122],"patients":[123],"from":[124],"healthy":[125],"controls.":[126]},"counts_by_year":[{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
