{"id":"https://openalex.org/W3004436874","doi":"https://doi.org/10.1109/bibm47256.2019.8983268","title":"Single-cell RNAseq Imputation Based on Matrix Completion with Side Information","display_name":"Single-cell RNAseq Imputation Based on Matrix Completion with Side Information","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3004436874","doi":"https://doi.org/10.1109/bibm47256.2019.8983268","mag":"3004436874"},"language":"en","primary_location":{"id":"doi:10.1109/bibm47256.2019.8983268","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983268","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 Bioinformatics and Biomedicine (BIBM)","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/A5100417442","display_name":"Tianyu Wang","orcid":"https://orcid.org/0000-0003-3944-6395"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tianyu Wang","raw_affiliation_strings":["University of Connecticut,Computer Science and Engineering,Storrs,US","Computer Science and Engineering, University of Connecticut, Storrs, US"],"affiliations":[{"raw_affiliation_string":"University of Connecticut,Computer Science and Engineering,Storrs,US","institution_ids":["https://openalex.org/I140172145"]},{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, US","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010254642","display_name":"Sheida Nabavi","orcid":"https://orcid.org/0000-0002-5996-1020"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheida Nabavi","raw_affiliation_strings":["University of Connecticut,Computer Science and Engineering,Storrs,US","Computer Science and Engineering, University of Connecticut, Storrs, US"],"affiliations":[{"raw_affiliation_string":"University of Connecticut,Computer Science and Engineering,Storrs,US","institution_ids":["https://openalex.org/I140172145"]},{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, US","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100417442"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13793702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2763","last_page":"2770"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9789999723434448,"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/T12676","display_name":"Machine Learning and ELM","score":0.957099974155426,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8603287935256958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6290198564529419},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5825353264808655},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.49752262234687805},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42518168687820435},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3203282356262207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2927846312522888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25010526180267334},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.22054368257522583},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17831209301948547}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8603287935256958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290198564529419},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5825353264808655},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.49752262234687805},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42518168687820435},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3203282356262207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2927846312522888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25010526180267334},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.22054368257522583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17831209301948547}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm47256.2019.8983268","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983268","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 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":36,"referenced_works":["https://openalex.org/W1940008012","https://openalex.org/W1967327758","https://openalex.org/W2033072655","https://openalex.org/W2060204507","https://openalex.org/W2069295116","https://openalex.org/W2106005123","https://openalex.org/W2108753466","https://openalex.org/W2111882541","https://openalex.org/W2123688186","https://openalex.org/W2134332047","https://openalex.org/W2152692691","https://openalex.org/W2164354502","https://openalex.org/W2187089797","https://openalex.org/W2516756255","https://openalex.org/W2557895864","https://openalex.org/W2602582876","https://openalex.org/W2611328865","https://openalex.org/W2751468300","https://openalex.org/W2792693509","https://openalex.org/W2799922543","https://openalex.org/W2888302115","https://openalex.org/W2898718502","https://openalex.org/W2899427022","https://openalex.org/W2949148097","https://openalex.org/W2951217100","https://openalex.org/W2951890476","https://openalex.org/W2952303649","https://openalex.org/W3003185417","https://openalex.org/W4235169531","https://openalex.org/W4289329247","https://openalex.org/W6640485552","https://openalex.org/W6675979915","https://openalex.org/W6676253046","https://openalex.org/W6730063867","https://openalex.org/W6755992330","https://openalex.org/W6759217680"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W4211215373","https://openalex.org/W3217094455","https://openalex.org/W2989589450","https://openalex.org/W3119637569","https://openalex.org/W2405773734","https://openalex.org/W2791189374","https://openalex.org/W3123325766","https://openalex.org/W2058928557"],"abstract_inverted_index":{"Drop-out":[0],"events":[1],"in":[2,12,35,145,149],"single-cell":[3,23],"RNA":[4],"sequencing":[5],"cause":[6],"large":[7],"numbers":[8],"of":[9,22,53,81,90,104,121,132,138,147],"zero":[10,33,91,142,166,178],"values":[11,17,34],"gene":[13,62,73,82,106,110],"expression":[14,83,111],"matrices.":[15,84,112],"Zero":[16],"hinder":[18],"accurate":[19,79,165],"down-stream":[20,150],"analysis":[21],"RNAseq":[24],"(scRNAseq)":[25],"data.":[26],"In":[27],"this":[28],"study,":[29],"to":[30,59,99,107,117,174],"estimate":[31,100],"the":[32,45,54,61,72,88,101,119,122,130,133,156,175],"scRNAseq":[36],"data,":[37],"we":[38,93],"proposed":[39,55,123,134,157],"a":[40,96],"novel":[41],"method":[42,56,135,158],"based":[43],"on":[44],"low":[46],"rank":[47],"matrix":[48],"completion":[49],"approach.":[50],"The":[51],"novelty":[52],"is":[57],"due":[58],"using":[60,125],"association":[63,74],"information":[64,75],"as":[65],"side":[66],"information.":[67],"We":[68,113,128],"observed":[69],"that":[70,137,155],"incorporating":[71],"can":[76],"facilitate":[77],"more":[78],"recovery":[80],"To":[85],"further":[86],"improve":[87],"accuracy":[89,148],"imputation,":[92],"additionally":[94],"employed":[95],"statistical":[97],"model":[98],"drop-out":[102],"probability":[103],"each":[105],"adjust":[108],"imputed":[109],"conducted":[114],"extensive":[115],"experiments":[116],"evaluate":[118],"performance":[120,131],"approach":[124],"several":[126],"datasets.":[127],"compared":[129,173],"with":[136],"three":[139],"commonly":[140,176],"used":[141,177],"imputation":[143,167,179],"methods":[144],"terms":[146],"clustering":[151],"analysis.":[152],"Results":[153],"show":[154],"has":[159,169],"higher":[160],"or":[161],"comparable":[162],"power":[163],"for":[164],"while":[168],"shorter":[170],"run":[171],"time":[172],"methods.":[180]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}