{"id":"https://openalex.org/W4413805655","doi":"https://doi.org/10.1186/s13040-025-00474-5","title":"Improving classification on imbalanced genomic data via KDE\u2013based synthetic sampling","display_name":"Improving classification on imbalanced genomic data via KDE\u2013based synthetic sampling","publication_year":2025,"publication_date":"2025-08-29","ids":{"openalex":"https://openalex.org/W4413805655","doi":"https://doi.org/10.1186/s13040-025-00474-5","pmid":"https://pubmed.ncbi.nlm.nih.gov/40883844"},"language":"en","primary_location":{"id":"doi:10.1186/s13040-025-00474-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00474-5","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00474-5","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"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":"BioData Mining","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00474-5","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117484481","display_name":"Edoardo Taccaliti","orcid":null},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Edoardo Taccaliti","raw_affiliation_strings":["Department of Biology, University of Naples Federico II, Naples, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biology, University of Naples Federico II, Naples, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049817649","display_name":"Jes\u00fas S. Aguilar\u2013Ruiz","orcid":"https://orcid.org/0000-0002-2666-293X"},"institutions":[{"id":"https://openalex.org/I95013407","display_name":"Universidad Pablo de Olavide","ror":"https://ror.org/02z749649","country_code":"ES","type":"education","lineage":["https://openalex.org/I95013407"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Jesus S. Aguilar\u2013Ruiz","raw_affiliation_strings":["School of Engineering, Pablo de Olavide University, Sevilla, 41013, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Pablo de Olavide University, Sevilla, 41013, Spain","institution_ids":["https://openalex.org/I95013407"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049817649"],"corresponding_institution_ids":["https://openalex.org/I95013407"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":6.3081,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.96186108,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"18","issue":"1","first_page":"60","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9277999997138977,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.9161254167556763},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.743444561958313},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6690293550491333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6226108074188232},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5766153335571289},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5211491584777832},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5141087174415588},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48354506492614746},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4822445809841156},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47512561082839966},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43723464012145996},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.41494956612586975},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.26677775382995605},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19071078300476074},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.18400335311889648},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14420589804649353}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.9161254167556763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.743444561958313},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6690293550491333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6226108074188232},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5766153335571289},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5211491584777832},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5141087174415588},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48354506492614746},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4822445809841156},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47512561082839966},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43723464012145996},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.41494956612586975},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26677775382995605},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19071078300476074},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.18400335311889648},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14420589804649353},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1186/s13040-025-00474-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00474-5","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00474-5","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"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":"BioData Mining","raw_type":"journal-article"},{"id":"pmid:40883844","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40883844","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":"BioData mining","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:ade397d4f2de4f989f77d41fb65b1e02","is_oa":true,"landing_page_url":"https://doaj.org/article/ade397d4f2de4f989f77d41fb65b1e02","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Mining, Vol 18, Iss 1, Pp 1-16 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12395650","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12395650/","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Min","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s13040-025-00474-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00474-5","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00474-5","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"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":"BioData Mining","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320315062","display_name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413805655.pdf","grobid_xml":"https://content.openalex.org/works/W4413805655.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W204885769","https://openalex.org/W1941659294","https://openalex.org/W1965895350","https://openalex.org/W2053724458","https://openalex.org/W2087787741","https://openalex.org/W2104933073","https://openalex.org/W2118978333","https://openalex.org/W2119168155","https://openalex.org/W2122646361","https://openalex.org/W2142594886","https://openalex.org/W2148143831","https://openalex.org/W2150559772","https://openalex.org/W2152885278","https://openalex.org/W2158698691","https://openalex.org/W2341128841","https://openalex.org/W2533835508","https://openalex.org/W2540642777","https://openalex.org/W2800788706","https://openalex.org/W2911964244","https://openalex.org/W2916041164","https://openalex.org/W2936503027","https://openalex.org/W2974322776","https://openalex.org/W2991198822","https://openalex.org/W3015967287","https://openalex.org/W3126232929","https://openalex.org/W3215252950","https://openalex.org/W4213102919","https://openalex.org/W4221039822","https://openalex.org/W4290043430","https://openalex.org/W4378901723","https://openalex.org/W4382702338","https://openalex.org/W4396773505","https://openalex.org/W4396795984","https://openalex.org/W4399042068","https://openalex.org/W4402512051"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4401045170","https://openalex.org/W4409475292","https://openalex.org/W3172259201","https://openalex.org/W3184937791","https://openalex.org/W4366990902","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487"],"abstract_inverted_index":{"Class":[0],"imbalance":[1],"poses":[2],"a":[3,61,169],"serious":[4],"challenge":[5],"in":[6,11,46,140,157,187],"biomedical":[7],"machine":[8],"learning,":[9],"particularly":[10],"genomics,":[12],"where":[13,49],"datasets":[14,72,110],"are":[15],"characterized":[16],"by":[17,73],"extremely":[18],"high":[19],"dimensionality":[20],"and":[21,95,118,121,126,172,185],"very":[22],"limited":[23],"sample":[24],"sizes.":[25],"In":[26,56],"such":[27,82,145],"settings,":[28],"standard":[29],"classifiers":[30,113],"tend":[31],"to":[32,38,68,124,143],"favor":[33],"the":[34,87,92,149,163],"majority":[35],"class,":[36],"leading":[37],"biased":[39],"predictions":[40],"-":[41],"an":[42],"especially":[43,139],"problematic":[44],"issue":[45],"clinical":[47],"diagnostics":[48],"rare":[50],"conditions":[51],"must":[52],"not":[53],"be":[54],"overlooked.":[55],"this":[57],"study,":[58],"we":[59],"introduce":[60],"Kernel":[62],"Density":[63],"Estimation":[64],"(KDE)-based":[65],"oversampling":[66,134],"approach":[67,167],"rebalance":[69],"imbalanced":[70],"genomic":[71,109,177],"generating":[74],"synthetic":[75],"minority":[76,93],"class":[77,94],"samples.":[78],"Unlike":[79],"conventional":[80],"methods":[81],"as":[83,146],"SMOTE,":[84],"KDE":[85,133,153],"estimates":[86],"global":[88],"probability":[89],"distribution":[90],"of":[91,148],"resamples":[96],"accordingly,":[97],"avoiding":[98],"local":[99],"interpolation":[100],"pitfalls.":[101],"We":[102],"evaluate":[103],"our":[104],"method":[105],"on":[106],"15":[107],"real-world":[108],"using":[111],"three":[112],"-Na\u00efve":[114],"Bayes,":[115],"Decision":[116],"Trees,":[117],"Random":[119],"Forests-":[120],"compare":[122],"it":[123],"SMOTE":[125],"baseline":[127],"training.":[128],"Experimental":[129],"results":[130,156],"demonstrate":[131],"that":[132],"consistently":[135],"improves":[136],"classification":[137],"performance,":[138],"metrics":[141],"robust":[142],"imbalance,":[144],"AUC":[147],"IMCP":[150],"curve.":[151],"Notably,":[152],"achieves":[154],"superior":[155],"tree-based":[158],"models":[159],"while":[160],"dramatically":[161],"simplifying":[162],"sampling":[164],"process.":[165],"This":[166],"offers":[168],"statistically":[170],"grounded":[171],"effective":[173],"solution":[174],"for":[175,182],"balancing":[176],"datasets,":[178],"with":[179],"strong":[180],"potential":[181],"improving":[183],"fairness":[184],"accuracy":[186],"high-stakes":[188],"medical":[189],"decision-making.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":1}],"updated_date":"2026-09-02T07:38:04.252551","created_date":"2025-10-10T00:00:00"}
