{"id":"https://openalex.org/W7160961958","doi":"https://doi.org/10.1145/3794209.3794223","title":"Entropy-Based Detection of Unknown Cell Type Using Deep Learning on Single-Cell RNA-Seq Data","display_name":"Entropy-Based Detection of Unknown Cell Type Using Deep Learning on Single-Cell RNA-Seq Data","publication_year":2025,"publication_date":"2025-11-27","ids":{"openalex":"https://openalex.org/W7160961958","doi":"https://doi.org/10.1145/3794209.3794223"},"language":null,"primary_location":{"id":"doi:10.1145/3794209.3794223","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794209.3794223","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 12th International Conference on Biomedical and Bioinformatics Engineering","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3794209.3794223","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135959531","display_name":"Haruka Sunagawa","orcid":"https://orcid.org/0009-0007-5726-8867"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Haruka Sunagawa","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan"],"raw_orcid":"https://orcid.org/0009-0007-5726-8867","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069977608","display_name":"Hironori Shigeta","orcid":"https://orcid.org/0009-0007-2035-3232"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hironori Shigeta","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan"],"raw_orcid":"https://orcid.org/0009-0007-2035-3232","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122949580","display_name":"Shigeto Seno","orcid":null},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeto Seno","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3861-6444","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007909918","display_name":"Hideo Matsuda","orcid":"https://orcid.org/0000-0002-4701-3779"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Matsuda","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan"],"raw_orcid":"https://orcid.org/0000-0002-4701-3779","affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Osaka, Suita, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98285908"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.89594826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"77","last_page":"81"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9977999925613403,"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":0.9977999925613403,"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":0.0006000000284984708,"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/T11255","display_name":"Microfluidic and Bio-sensing Technologies","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7321000099182129},{"id":"https://openalex.org/keywords/cell-type","display_name":"Cell type","score":0.6049000024795532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4918000102043152},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/type","display_name":"Type (biology)","score":0.36730000376701355}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7908999919891357},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7321000099182129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6495000123977661},{"id":"https://openalex.org/C189014844","wikidata":"https://www.wikidata.org/wiki/Q189118","display_name":"Cell type","level":3,"score":0.6049000024795532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4918000102043152},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35100001096725464},{"id":"https://openalex.org/C1491633281","wikidata":"https://www.wikidata.org/wiki/Q7868","display_name":"Cell","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.28519999980926514},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2702000141143799}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3794209.3794223","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794209.3794223","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 12th International Conference on Biomedical and Bioinformatics Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3794209.3794223","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794209.3794223","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 12th International Conference on Biomedical and Bioinformatics Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5563675165176392,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G3070978093","display_name":null,"funder_award_id":"JP21K19827,JP24K01949","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2523620612","https://openalex.org/W2800392236","https://openalex.org/W2886347923","https://openalex.org/W2949829455","https://openalex.org/W4205428105","https://openalex.org/W4293515657","https://openalex.org/W4316096017","https://openalex.org/W4377029825","https://openalex.org/W4396242195","https://openalex.org/W4401441450","https://openalex.org/W4401455022"],"related_works":[],"abstract_inverted_index":{"Single-cell":[0],"RNA":[1],"sequencing":[2],"(scRNA-seq)":[3],"has":[4],"been":[5],"widely":[6],"used":[7,114],"in":[8,24,64,89,134],"many":[9],"fields":[10],"of":[11,29,60,140],"biological":[12],"research.":[13],"Identifying":[14],"cell":[15,41,61,76,80,104,122],"types":[16,62,81,105],"from":[17,83],"scRNA-seq":[18],"datasets":[19],"is":[20,71,113,144],"a":[21,30,107,116,121,130],"crucial":[22],"step":[23],"characterizing":[25],"the":[26,50,57,65,84,90,135,141],"cellular":[27],"composition":[28],"tissue":[31],"or":[32,128],"organ.":[33],"Machine":[34],"learning":[35,109,151],"approaches":[36],"are":[37],"often":[38],"applied":[39],"to":[40,73,118,124,129],"type":[42,127,132],"identification.":[43],"However,":[44],"these":[45],"methods":[46],"have":[47],"limitations,":[48],"as":[49,115],"reference":[51,85,136],"(training)":[52],"dataset":[53,86],"may":[54],"not":[55],"contain":[56],"full":[58],"repertoire":[59],"present":[63,88,133],"query":[66,91],"(test)":[67],"data.":[68,92],"Therefore,":[69],"it":[70],"important":[72],"identify":[74],"unknown":[75,103,126],"types,":[77],"that":[78],"is,":[79],"absent":[82],"but":[87],"In":[93],"this":[94],"study,":[95],"we":[96],"propose":[97],"an":[98,125],"entropy-based":[99],"method":[100,143],"for":[101],"detecting":[102],"within":[106],"deep":[108,150],"framework.":[110],"Information":[111],"entropy":[112],"criterion":[117],"determine":[119],"whether":[120],"belongs":[123],"known":[131],"dataset.":[137],"The":[138],"effectiveness":[139],"proposed":[142],"demonstrated":[145],"through":[146],"comparisons":[147],"with":[148],"other":[149],"approaches.":[152]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-13T00:00:00"}