{"id":"https://openalex.org/W4390971211","doi":"https://doi.org/10.1109/bibm58861.2023.10385409","title":"scNMF-Impute: imputation for single-cell RNA-seq data based on nonnegative matrix factorization","display_name":"scNMF-Impute: imputation for single-cell RNA-seq data based on nonnegative matrix factorization","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390971211","doi":"https://doi.org/10.1109/bibm58861.2023.10385409"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385409","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100347490","display_name":"Juan Wang","orcid":"https://orcid.org/0000-0003-3934-0435"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Wang","raw_affiliation_strings":["Qufu Normal University,School of Computer Science,Rizhao,China,276826"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,School of Computer Science,Rizhao,China,276826","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Na-Na Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na-Na Zhang","raw_affiliation_strings":["Qufu Normal University,School of Computer Science,Rizhao,China,276826"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,School of Computer Science,Rizhao,China,276826","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007539735","display_name":"Junliang Shang","orcid":"https://orcid.org/0000-0002-8488-2228"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun-Liang Shang","raw_affiliation_strings":["Qufu Normal University,School of Computer Science,Rizhao,China,276826"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,School of Computer Science,Rizhao,China,276826","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073144090","display_name":"Jin\u2010Xing Liu","orcid":"https://orcid.org/0000-0001-6104-2149"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin-Xing Liu","raw_affiliation_strings":["Qufu Normal University,School of Computer Science,Rizhao,China,276826"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,School of Computer Science,Rizhao,China,276826","institution_ids":["https://openalex.org/I202126657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I202126657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25625231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"3200","last_page":"3207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","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/T11289","display_name":"Single-cell and spatial transcriptomics","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/T10885","display_name":"Gene expression and cancer classification","score":0.9814000129699707,"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/T10062","display_name":"MicroRNA in disease regulation","score":0.9541000127792358,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"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/dropout","display_name":"Dropout (neural networks)","score":0.7929809093475342},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7771729230880737},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6946691274642944},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6772249341011047},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6711663007736206},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5109097361564636},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.510141909122467},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4614056646823883},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.41343945264816284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4120236337184906},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34085899591445923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25937288999557495},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08438259363174438}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7929809093475342},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7771729230880737},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6946691274642944},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6772249341011047},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711663007736206},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5109097361564636},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.510141909122467},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4614056646823883},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.41343945264816284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4120236337184906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34085899591445923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25937288999557495},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08438259363174438},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm58861.2023.10385409","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm58861.2023.10385409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1552434751","https://openalex.org/W1987246402","https://openalex.org/W2023887100","https://openalex.org/W2033072655","https://openalex.org/W2051658465","https://openalex.org/W2068881679","https://openalex.org/W2069295116","https://openalex.org/W2109890799","https://openalex.org/W2111882541","https://openalex.org/W2153344005","https://openalex.org/W2160197181","https://openalex.org/W2178888844","https://openalex.org/W2187089797","https://openalex.org/W2494395359","https://openalex.org/W2602582876","https://openalex.org/W2747545374","https://openalex.org/W2751468300","https://openalex.org/W2789949350","https://openalex.org/W2792693509","https://openalex.org/W2804460316","https://openalex.org/W2901838752","https://openalex.org/W2918882492","https://openalex.org/W2949067670","https://openalex.org/W2949148097","https://openalex.org/W2953251392","https://openalex.org/W3007573310","https://openalex.org/W3009560560","https://openalex.org/W3091601777"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Single-cell":[0],"RNA":[1],"sequencing":[2,17],"(scRNA-seq)":[3],"data":[4,57,145,184,225],"are":[5],"collected":[6],"at":[7],"an":[8],"unheard-of":[9],"rate":[10],"thanks":[11],"to":[12,21,31,153,206],"the":[13,22,33,50,60,67,91,99,109,112,116,119,124,131,140,143,148,155,163,169,180,191,208,233],"advancement":[14],"of":[15,24,41,69,101,111,118,142,157,174,183,210,235],"high-throughput":[16],"technologies.":[18],"However,":[19],"due":[20],"limitations":[23],"current":[25],"technology,":[26],"scRNA-seq":[27,204],"is":[28,137],"sometimes":[29],"unable":[30],"capture":[32],"expressed":[34],"genes,":[35],"resulting":[36],"in":[37,49,59,76,190],"a":[38,81,128],"large":[39],"number":[40],"zero":[42],"counts":[43],"(also":[44],"known":[45],"as":[46,127,139],"dropout":[47,53,92,113,125,136,149,158,188],"events)":[48],"data.":[51],"These":[52],"events":[54,93,126,189],"can":[55,178,221],"cause":[56],"loss":[58],"gene":[61,132,175,192,229],"expression":[62,133,193],"matrix":[63,103,134,146],"and":[64,94,114,147,202,212,226,239],"severely":[65],"hampers":[66],"accuracy":[68,234],"downstream":[70],"analysis.":[71],"To":[72,106],"address":[73],"this":[74,77],"problem,":[75],"paper,":[78],"we":[79,121,161],"propose":[80],"new":[82],"imputation":[83],"method":[84,89],"called":[85],"scNMF-impute.":[86],"The":[87,172,216],"scNMF-impute":[88,211,220],"imputes":[90],"performs":[95],"dimensionality":[96],"reduction":[97],"under":[98],"framework":[100],"nonnegative":[102],"factorization":[104],"(NMF).":[105],"effectively":[107],"identify":[108],"location":[110],"recover":[115],"value":[117],"dropout,":[120],"explicitly":[122],"model":[123],"matrix.":[129,150,194],"Therefore,":[130],"without":[135],"represented":[138],"sum":[141],"original":[144],"In":[151],"addition,":[152],"reduce":[154],"influence":[156],"on":[159,199],"factorization,":[160],"introduce":[162],"similarity":[164,176],"information":[165,177],"between":[166],"genes":[167],"into":[168],"NMF":[170],"model.":[171],"introduction":[173],"ensure":[179],"accurate":[181,242],"recovery":[182],"structures":[185],"obscured":[186],"by":[187],"We":[195],"conducted":[196],"extensive":[197],"experiments":[198],"simulated":[200],"datasets":[201,205],"real":[203],"verify":[207],"effectiveness":[209],"other":[213],"state-of-the-art":[214],"methods.":[215],"results":[217],"show":[218],"that":[219],"accurately":[222],"calculate":[223],"missing":[224],"restore":[227],"true":[228],"expression,":[230],"thus":[231],"improving":[232],"existing":[236],"clustering":[237,244],"methods":[238],"obtaining":[240],"more":[241],"cell":[243],"results.":[245]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}