Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8317
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dc.contributor.authorÖzgül, Ozan Frat-
dc.contributor.authorBardak, Batuhan-
dc.contributor.authorTan, Mehmet-
dc.date.accessioned2022-01-15T13:02:30Z-
dc.date.available2022-01-15T13:02:30Z-
dc.date.issued2021-
dc.identifier.issn1545-5963-
dc.identifier.issn1557-9964-
dc.identifier.urihttps://doi.org/10.1109/TCBB.2020.2988985-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8317-
dc.description.abstractThe functional or regulatory processes within the cell are explicitly governed by the expression levels of a subset of its genes. Gene expression time series captures activities of individual genes over time and aids revealing underlying cellular dynamics. An important step in high-throughput gene expression time series experiment is clustering genes based on their temporal expression patterns and is conventionally achieved by unsupervised machine learning techniques. However, most of the clustering techniques either suffer from the short length of gene expression time series or ignore temporal structure of the data. In this work, we propose DeepTrust, a novel deep learning-based framework for gene expression time series clustering which can overcome these issues. DeepTrust initially transforms time series data into images to obtain richer data representations. Afterwards, a deep convolutional clustering algorithm is applied on the constructed images. Analyses on both simulated and biological data sets exhibit the efficiency of this new framework, compared to widely used clustering techniques. We also utilize enrichment analyses to illustrate the biological plausibility of the clusters detected by DeepTrust. Our code and data are available from http://github.com/tanlab/DeepTrust.en_US
dc.language.isoenen_US
dc.publisherIeee Computer Socen_US
dc.relation.ispartofIeee-Acm Transactions on Computational Biology and Bioinformaticsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTime series analysisen_US
dc.subjectGene expressionen_US
dc.subjectMachine learningen_US
dc.subjectClustering algorithmsen_US
dc.subjectBiological system modelingen_US
dc.subjectTrajectoryen_US
dc.subjectBiological information theoryen_US
dc.subjectGene expressionen_US
dc.subjectclusteringen_US
dc.subjectrecurrence plotsen_US
dc.subjectdeep learningen_US
dc.subjectNf-Kappa-Ben_US
dc.subjectHelicobacter-Pylorien_US
dc.subjectRecurrence Ploten_US
dc.titleA Convolutional Deep Clustering Framework for Gene Expression Time Seriesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume18en_US
dc.identifier.issue6en_US
dc.identifier.startpage2198en_US
dc.identifier.endpage2207en_US
dc.authoridOzgul, Ozan / 0000-0002-4530-6674-
dc.identifier.wosWOS:000728193500016en_US
dc.identifier.scopus2-s2.0-85121682620en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.pmid32324563en_US
dc.identifier.doi10.1109/TCBB.2020.2988985-
dc.authorscopusid57202866539-
dc.authorscopusid57188767392-
dc.authorscopusid36984623900-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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