{"id":"https://openalex.org/W7126108821","doi":"https://doi.org/10.1109/bibm66473.2025.11356225","title":"Scdiffae: a Diffusion Autoencoder Framework for Clustering Scrna-Seq Data","display_name":"Scdiffae: a Diffusion Autoencoder Framework for Clustering Scrna-Seq Data","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126108821","doi":"https://doi.org/10.1109/bibm66473.2025.11356225"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5121115469","display_name":"Yang E. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I21642278","display_name":"Ningxia University","ror":"https://ror.org/04j7b2v61","country_code":"CN","type":"education","lineage":["https://openalex.org/I21642278"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Li","raw_affiliation_strings":["School of Information Engineering, Ningxia University,Yinchuan,China,750021"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Ningxia University,Yinchuan,China,750021","institution_ids":["https://openalex.org/I21642278"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124152316","display_name":"Junlei Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I21642278","display_name":"Ningxia University","ror":"https://ror.org/04j7b2v61","country_code":"CN","type":"education","lineage":["https://openalex.org/I21642278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junlei Zhou","raw_affiliation_strings":["School of Information Engineering, Ningxia University,Yinchuan,China,750021"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Ningxia University,Yinchuan,China,750021","institution_ids":["https://openalex.org/I21642278"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114025829","display_name":"Zhenhua Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I21642278","display_name":"Ningxia University","ror":"https://ror.org/04j7b2v61","country_code":"CN","type":"education","lineage":["https://openalex.org/I21642278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhua Yu","raw_affiliation_strings":["School of Information Engineering, Ningxia University,Yinchuan,China,750021"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Ningxia University,Yinchuan,China,750021","institution_ids":["https://openalex.org/I21642278"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121115469"],"corresponding_institution_ids":["https://openalex.org/I21642278"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65234299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"519","last_page":"525"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9987000226974487,"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":0.9987000226974487,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":9.999999747378752e-05,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6962000131607056},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5799999833106995},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5669000148773193},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.375},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.3621000051498413},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.353300005197525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3458000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7396000027656555},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6962000131607056},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5799999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5691999793052673},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.522599995136261},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36570000648498535},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.26930001378059387},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5103070055","display_name":null,"funder_award_id":"2023AAC05006","funder_id":"https://openalex.org/F4320322877","funder_display_name":"Natural Science Foundation of Ningxia Province"}],"funders":[{"id":"https://openalex.org/F4320322877","display_name":"Natural Science Foundation of Ningxia Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1631320694","https://openalex.org/W1901129140","https://openalex.org/W1982980578","https://openalex.org/W1990523483","https://openalex.org/W2069089843","https://openalex.org/W2100495367","https://openalex.org/W2373831850","https://openalex.org/W2562003322","https://openalex.org/W2598326928","https://openalex.org/W2736547131","https://openalex.org/W2752378502","https://openalex.org/W2795687816","https://openalex.org/W2887326710","https://openalex.org/W2894687190","https://openalex.org/W2895456557","https://openalex.org/W2902652978","https://openalex.org/W2915419035","https://openalex.org/W2937917790","https://openalex.org/W2951381561","https://openalex.org/W2951506174","https://openalex.org/W2997470228","https://openalex.org/W3024013192","https://openalex.org/W3029148360","https://openalex.org/W3138479716","https://openalex.org/W3153378165","https://openalex.org/W3175272474","https://openalex.org/W3217030260","https://openalex.org/W4225343266","https://openalex.org/W4315490524","https://openalex.org/W4317888741","https://openalex.org/W4319866019","https://openalex.org/W4401103220","https://openalex.org/W4401794004","https://openalex.org/W4403198130","https://openalex.org/W4410359280","https://openalex.org/W4411256976"],"related_works":[],"abstract_inverted_index":{"Single-cell":[0],"RNA":[1],"sequencing":[2],"(scRNA-seq)":[3],"enables":[4],"comprehensive":[5],"investigation":[6],"of":[7,43,56,168],"cellular":[8],"heterogeneity":[9],"at":[10],"the":[11,15,40,106,132,135,166,169],"transcriptomic":[12],"level.":[13],"Nevertheless,":[14],"high":[16],"dimensionality,":[17],"sparsity,":[18],"and":[19,88,99,120,158],"technical":[20,60,114],"noise":[21],"inherent":[22],"to":[23,38,83,103,129],"scRNA-seq":[24],"data":[25],"pose":[26],"significant":[27],"challenges":[28],"for":[29,176],"precise":[30],"cell":[31],"type":[32],"identification.":[33],"Traditional":[34],"clustering":[35,71,170],"methods":[36,151],"struggle":[37],"capture":[39],"nonlinear":[41],"structures":[42],"such":[44],"data,":[45,108],"while":[46],"existing":[47],"deep":[48],"learning":[49],"approaches":[50],"commonly":[51],"suffer":[52],"from":[53,59,113,134],"inadequate":[54],"disentanglement":[55,119],"semantic":[57,81,86,98,111,122,136],"features":[58,102,112],"noise.":[61,115],"To":[62,116],"mitigate":[63],"these":[64],"limitations,":[65],"we":[66],"present":[67],"scDiffAE,":[68],"a":[69,75,80,90,173],"new":[70],"approach":[72],"built":[73],"upon":[74],"diffusion":[76,92],"autoencoder.":[77],"It":[78],"leverages":[79],"encoder":[82],"extract":[84],"low-dimensional":[85],"embeddings,":[87],"incorporates":[89],"conditional":[91],"model":[93],"that":[94,145],"conditions":[95],"on":[96,139],"both":[97],"random":[100],"encoding":[101],"progressively":[104],"reconstruct":[105,131],"original":[107],"effectively":[109],"decoupling":[110],"further":[117],"promote":[118],"preserve":[121],"integrity,":[123],"an":[124],"auxiliary":[125],"decoder":[126],"is":[127],"introduced":[128],"directly":[130],"input":[133],"embeddings.":[137],"Evaluations":[138],"11":[140],"real-world":[141],"scRNAseq":[142],"datasets":[143],"demonstrate":[144],"scDiffAE":[146],"consistently":[147],"surpasses":[148],"seven":[149],"leading":[150],"across":[152],"multiple":[153],"metrics,":[154],"including":[155],"ARI,":[156],"AMI,":[157],"NMI.":[159],"Further":[160],"differential":[161],"gene":[162],"expression":[163],"analysis":[164],"validates":[165],"reliability":[167],"results,":[171],"providing":[172],"solid":[174],"foundation":[175],"downstream":[177],"research.":[178]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}