{"id":"https://openalex.org/W4399144449","doi":"https://doi.org/10.1145/3654823.3654881","title":"Unsupervised Deep Topology Embedded Characterization of Single-Cell Chromatin Accessibility Profiles","display_name":"Unsupervised Deep Topology Embedded Characterization of Single-Cell Chromatin Accessibility Profiles","publication_year":2024,"publication_date":"2024-03-22","ids":{"openalex":"https://openalex.org/W4399144449","doi":"https://doi.org/10.1145/3654823.3654881"},"language":"en","primary_location":{"id":"doi:10.1145/3654823.3654881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","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/A5021301231","display_name":"Tairan Jing","orcid":"https://orcid.org/0009-0006-6896-5345"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tairan Jing","raw_affiliation_strings":["Jilin University, China"],"raw_orcid":"https://orcid.org/0009-0006-6896-5345","affiliations":[{"raw_affiliation_string":"Jilin University, China","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5021301231"],"corresponding_institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210136497"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07706858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"315","last_page":"322"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9955000281333923,"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/T10222","display_name":"Genomics and Chromatin Dynamics","score":0.9922999739646912,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.689714789390564},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6342975497245789},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5937523245811462},{"id":"https://openalex.org/keywords/chromatin","display_name":"Chromatin","score":0.5493049025535583},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.5383279323577881},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5060610175132751},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4909818470478058},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.46980080008506775},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45108044147491455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.420209676027298},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4164181351661682},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3766748607158661},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3515801727771759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32319921255111694},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16354158520698547},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10270431637763977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.689714789390564},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6342975497245789},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5937523245811462},{"id":"https://openalex.org/C83640560","wikidata":"https://www.wikidata.org/wiki/Q180951","display_name":"Chromatin","level":3,"score":0.5493049025535583},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.5383279323577881},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5060610175132751},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4909818470478058},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.46980080008506775},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45108044147491455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.420209676027298},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4164181351661682},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3766748607158661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3515801727771759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32319921255111694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16354158520698547},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10270431637763977},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C552990157","wikidata":"https://www.wikidata.org/wiki/Q7430","display_name":"DNA","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3654823.3654881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","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":22,"referenced_works":["https://openalex.org/W1492083415","https://openalex.org/W1602702667","https://openalex.org/W1850642846","https://openalex.org/W1998871699","https://openalex.org/W2071128523","https://openalex.org/W2079361215","https://openalex.org/W2436634098","https://openalex.org/W2750049634","https://openalex.org/W2887910040","https://openalex.org/W2901677030","https://openalex.org/W2934716754","https://openalex.org/W2937917790","https://openalex.org/W2951054376","https://openalex.org/W2953322088","https://openalex.org/W2978541146","https://openalex.org/W2979464975","https://openalex.org/W3137285774","https://openalex.org/W4220680295","https://openalex.org/W4221166060","https://openalex.org/W4283815719","https://openalex.org/W4317888741","https://openalex.org/W4382239909"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W3163146846"],"abstract_inverted_index":{"Cell":[0],"clustering":[1],"plays":[2],"a":[3,71,83,89,112,150,188],"crucial":[4],"role":[5],"in":[6,82],"the":[7,61,117,120,125,129,134,140,144,157,164,171,183],"analysis":[8],"of":[9,74,159,166,174,185,190],"single-cell":[10],"Assay":[11],"for":[12,27],"Transposase-Accessible":[13],"Chromatin":[14],"using":[15],"sequencing":[16],"(scATAC-seq)":[17],"data.":[18],"Single-cell":[19],"deep":[20],"learning":[21,130,145],"models":[22,38],"have":[23],"gained":[24],"significant":[25],"popularity":[26],"characterizing":[28],"low-dimensional":[29,84],"embedding":[30,158],"feature":[31],"representations":[32,99],"to":[33,41,93,115,154],"facilitate":[34],"clustering.":[35],"However,":[36],"these":[37,57],"are":[39],"prone":[40],"technical":[42],"artifacts,":[43],"noise,":[44],"and":[45,78,128,138,163,179],"missing":[46],"values,":[47],"which":[48,69],"can":[49],"negatively":[50],"affect":[51],"their":[52],"overall":[53],"performance.":[54],"To":[55],"address":[56],"limitations,":[58],"we":[59],"propose":[60],"Single-Cell":[62],"Deep":[63],"Topology":[64],"Embedded":[65],"Characterization":[66],"(scDTEC)":[67],"model,":[68],"obtains":[70],"fused":[72],"representation":[73],"chromatin":[75,102],"accessibility":[76,103],"profiles":[77,104],"cell":[79,160,167,175],"topological":[80,107],"information":[81,162],"space.":[85],"First,":[86],"scDTEC":[87,110,148,186],"employs":[88,111,149],"topology":[90,168],"variational":[91],"autoencoder":[92],"transform":[94],"high-dimensional":[95],"data":[96,127],"into":[97],"latent":[98],"that":[100],"capture":[101],"with":[105],"cellular":[106],"information.":[108],"Then,":[109],"contrastive":[113],"loss":[114],"maximize":[116],"consistency":[118],"between":[119],"anchor":[121,141],"graph":[122,131,135,142],"derived":[123],"from":[124],"raw":[126],"generated":[132],"by":[133],"learner":[136],"model":[137],"uses":[139],"as":[143],"objective.":[146],"Finally,":[147],"joint":[151],"optimization":[152],"paradigm":[153],"simultaneously":[155],"optimize":[156],"fusion":[161],"updating":[165],"structure,":[169],"guiding":[170],"precise":[172],"partitioning":[173],"clusters.":[176],"Real-data":[177],"experiments":[178],"extensive":[180],"simulation":[181],"reveal":[182],"superiority":[184],"over":[187],"variety":[189],"cutting-edge":[191],"methods.":[192]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
