{"id":"https://openalex.org/W4390992213","doi":"https://doi.org/10.1109/bibm58861.2023.10385264","title":"scBERC: A Batch Effect-Removed Clustering method for single-cell omics","display_name":"scBERC: A Batch Effect-Removed Clustering method for single-cell omics","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390992213","doi":"https://doi.org/10.1109/bibm58861.2023.10385264"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm58861.2023.10385264","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":"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/A5112297954","display_name":"Yitao Zhou","orcid":"https://orcid.org/0009-0004-9413-8218"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yitao Zhou","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100347225","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0003-2736-6419"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101989504","display_name":"Feng Zeng","orcid":"https://orcid.org/0000-0001-6469-9541"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zeng","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China","State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Research Unit of Cellular Stress of CAMS, Cancer Research Center, School of Medicine, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Research Unit of Cellular Stress of CAMS, Cancer Research Center, School of Medicine, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035880695","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0003-4821-6583"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine, Xiamen Key Laboratory of Big Data Intelligent Analysis &#x0026; Decision,Department of Automation,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33277675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"359","last_page":"364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":1.0,"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":1.0,"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":0.9800999760627747,"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/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9613000154495239,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8815182447433472},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7370280623435974},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5233206748962402},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4682045578956604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3417976498603821},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32027941942214966}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8815182447433472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7370280623435974},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5233206748962402},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4682045578956604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3417976498603821},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32027941942214966}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm58861.2023.10385264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm58861.2023.10385264","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":[{"id":"https://openalex.org/F4320316884","display_name":"Natural Science Foundation of Xiamen City","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329791","display_name":"Shenzhen Fundamental Research Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1523794535","https://openalex.org/W1580018887","https://openalex.org/W2018050443","https://openalex.org/W2523620612","https://openalex.org/W2526262591","https://openalex.org/W2752378502","https://openalex.org/W2794480084","https://openalex.org/W2794521141","https://openalex.org/W2894687190","https://openalex.org/W2924067995","https://openalex.org/W2937917790","https://openalex.org/W3021272327","https://openalex.org/W3111450319","https://openalex.org/W3134128419","https://openalex.org/W3164852867","https://openalex.org/W4295717668","https://openalex.org/W4311911704","https://openalex.org/W4312426768","https://openalex.org/W4315621823","https://openalex.org/W4319866019","https://openalex.org/W4361190647"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Single-cell":[0],"clustering":[1,108,137,141],"is":[2],"a":[3,48,55],"pivotal":[4],"technique":[5],"in":[6,27],"deciphering":[7],"single-cell":[8,28,40,57,129,136],"omics":[9,29],"data,":[10],"enabling":[11],"us":[12],"to":[13,34,110],"gain":[14],"insights":[15],"into":[16],"cell":[17,77],"function":[18],"and":[19,37,60,84,113,143],"heterogeneity.":[20],"However,":[21],"the":[22,35,73,91,95,105,118],"presence":[23],"of":[24,39,76],"batch":[25,122],"effects":[26],"data":[30,93],"poses":[31],"significant":[32],"challenges":[33],"accuracy":[36],"reliability":[38],"clustering.":[41],"To":[42],"address":[43],"these":[44],"challenges,":[45],"we":[46],"develop":[47],"Batch":[49],"Effect-Removed":[50],"Clustering":[51],"method(scBERC)":[52],"that":[53,132],"incorporates":[54],"novel":[56],"augmentation":[58,88],"strategy":[59,89,98],"contrastive":[61,96],"learning.":[62],"By":[63],"augmenting":[64],"cells":[65],"with":[66],"artificially":[67],"induced":[68],"variations,":[69],"our":[70],"approach":[71],"captures":[72],"full":[74],"range":[75],"states,":[78],"accounting":[79],"for":[80],"both":[81],"biological":[82,102],"heterogeneity":[83],"batch-specific":[85],"variations.":[86],"This":[87],"enriches":[90],"training":[92],"while":[94],"learning":[97],"better":[99],"extracts":[100],"real":[101,128],"features,":[103],"allowing":[104],"deep":[106],"subspace":[107],"algorithm":[109],"learn":[111],"robust":[112],"generalized":[114],"patterns,":[115],"thereby":[116],"mitigating":[117],"biases":[119],"introduced":[120],"by":[121],"effects.":[123],"Experimental":[124],"results":[125],"on":[126],"twelve":[127],"datasets":[130],"demonstrate":[131],"scBERC":[133],"outperforms":[134],"existing":[135],"methods,":[138],"significantly":[139],"improving":[140],"performance":[142],"facilitating":[144],"downstream":[145],"analysis.":[146]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
