1. Электронный научный архив УрФУ
  2. 00. Общеуниверситетские коллекции
  3. Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC
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dc.contributor.authorDordiuk, V.en
dc.contributor.authorDemicheva, E.en
dc.contributor.authorEspino, F. P.en
dc.contributor.authorUshenin, K.en
dc.date.accessioned2024-04-08T11:07:10Z-
dc.date.available2024-04-08T11:07:10Z-
dc.date.issued2022-
dc.identifier.citationDordiuk, V, Demicheva, E, Espino, FP & Ushenin, K 2022, Natural language processing for clusterization of genes according to their functions. в Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022. Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022, Institute of Electrical and Electronics Engineers Inc., стр. 1-4. https://doi.org/10.1109/CSGB56354.2022.9865330harvard_pure
dc.identifier.citationDordiuk, V., Demicheva, E., Espino, F. P., & Ushenin, K. (2022). Natural language processing for clusterization of genes according to their functions. в Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022 (стр. 1-4). (Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB56354.2022.9865330apa_pure
dc.identifier.isbn978-166545288-5-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access; Green Open Access3
dc.identifier.otherhttps://arxiv.org/pdf/2207.081621
dc.identifier.otherhttps://arxiv.org/pdf/2207.08162pdf
dc.identifier.urihttp://elar.urfu.ru/handle/10995/131416-
dc.description.abstractThere are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches. The encoded gene function pass through the dimensionality reduction and clusterization. Aiming to find the most efficient pipeline, 180 cases of pipeline with different methods in the major pipeline steps were analyzed. The performance was evaluated with clusterization indexes and expert review of the results. © 2022 IEEE.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.source2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)2
dc.sourceProceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022en
dc.subjectBERTen
dc.subjectCLUSTERIZATIONen
dc.subjectDIFFERENTIAL GENE EXPRESSION ANALYSISen
dc.subjectGENE EXPRESSIONen
dc.subjectGENE ONTOLOGYen
dc.subjectNATURAL LANGUAGE PROCESSINGen
dc.subjectSEMANTIC ANALYSISen
dc.subjectGENE ONTOLOGYen
dc.subjectNATURAL LANGUAGE PROCESSING SYSTEMSen
dc.subjectPIPELINESen
dc.subjectSEMANTICSen
dc.subjectTEXT PROCESSINGen
dc.subjectBERTen
dc.subjectCLUSTERIZATIONen
dc.subjectDIFFERENTIAL GENE EXPRESSION ANALYSEen
dc.subjectDIFFERENTIAL GENE EXPRESSIONSen
dc.subjectGENE EXPRESSION ANALYSISen
dc.subjectGENE ONTOLOGYen
dc.subjectGENES EXPRESSIONen
dc.subjectLANGUAGE PROCESSINGen
dc.subjectNATURAL LANGUAGE PROCESSINGen
dc.subjectNATURAL LANGUAGESen
dc.subjectSEMANTIC ANALYSISen
dc.subjectGENE EXPRESSIONen
dc.titleNatural language processing for clusterization of genes according to their functionsen
dc.typeConference paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/submittedVersionen
dc.conference.name7 July 2022 through 8 July 2022en
dc.conference.date2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022-
dc.identifier.doi10.1109/CSGB56354.2022.9865330-
dc.identifier.scopus85138478040-
local.contributor.employeeDordiuk V., Institute of Immunology and Physiology, Ekaterinburg, Russian Federation, Ural Federal University, Ekaterinburg, Russian Federationen
local.contributor.employeeDemicheva E., Institute of Immunology and Physiology, Ekaterinburg, Russian Federation, Ural Federal University, Ekaterinburg, Russian Federationen
local.contributor.employeeEspino F.P., Ural Federal University, Ekaterinburg, Russian Federationen
local.contributor.employeeUshenin K., Institute of Immunology and Physiology, Ekaterinburg, Russian Federation, Ural Federal University, Ekaterinburg, Russian Federationen
local.description.firstpage1-
local.description.lastpage4-
local.contributor.departmentInstitute of Immunology and Physiology, Ekaterinburg, Russian Federationen
local.contributor.departmentUral Federal University, Ekaterinburg, Russian Federationen
local.identifier.pure30979989-
local.identifier.pure09b7ecd7-fbc7-4fce-a67d-a7d6086d37f3uuid
local.identifier.eid2-s2.0-85138478040-
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