Advanced Search

Show simple item record

dc.contributor.authorSingh, Sumeet Pal
dc.contributor.authorJanjuha, Sharan
dc.contributor.authorChaudhuri, Samata
dc.contributor.authorReinhardt, Susanne
dc.contributor.authorKränkel, Annekathrin
dc.contributor.authorDietz, Sevina
dc.contributor.authorEugster, Anne
dc.contributor.authorBilgin, Halil
dc.contributor.authorKorkmaz, Selçuk
dc.contributor.authorZararsız, Gökmen
dc.contributor.authorNinov, Nikolay
dc.contributor.authorReid, John E.
dc.date.accessioned2019-06-25T09:12:15Z
dc.date.available2019-06-25T09:12:15Z
dc.date.issued2018en_US
dc.identifier.citationSCIENTIFIC REPORTS Volume: 8 Article Number: 17156 DOI: 10.1038/s41598-018-35218-5en_US
dc.identifier.issn2045-2322
dc.identifier.otherAccession Number: WOS:000450766300014
dc.identifier.otherPubMed ID: 30464314
dc.identifier.urihttp://acikerisim.agu.edu.tr/xmlui/handle/20.500.12573/33
dc.description.abstractAge-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.en_US
dc.description.sponsorshipCRTD postdoctoral seed grant CRTD -FZ 111 DFG-Center for Regenerative Therapies Dresden EFSD/Lilly Young Investigator Program
dc.language.isoengen_US
dc.publisherNATURE PUBLISHING GROUP, MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLANDen_US
dc.relation.ispartofseriesSCIENTIFIC REPORTS;Volume: 8 Article Number: 17156
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPANCREATIC-ISLETSen_US
dc.subjectDYNAMICSen_US
dc.subjectSIGNATURESen_US
dc.subjectCYCLEen_US
dc.titleMachine learning based classifcation of cells into chronological stages using singlecell transcriptomicsen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthor
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record