Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/329
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dc.contributor.authorLyu, Minjieen_US
dc.contributor.authorZhang, Yihanen_US
dc.contributor.authorYang, Luningen_US
dc.contributor.authorLin, Xinen_US
dc.contributor.authorLi, Yilinen_US
dc.contributor.authorJin, Huanen_US
dc.contributor.authorBellotti, Anthony G.en_US
dc.contributor.authorMitić, Nenaden_US
dc.contributor.authorBrusic, Vladimiren_US
dc.date.accessioned2022-08-09T12:48:36Z-
dc.date.available2022-08-09T12:48:36Z-
dc.date.issued2021-01-01-
dc.identifier.isbn9781665401265-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/329-
dc.description.abstractWe performed classification of healthy Peripheral Blood Mononuclear Cells cell types using four methods Artificial Neural Network (ANN), Profiles, Protein Markers (PMs), and RNA markers (RNAMs). Profiles represent patterns of gene expressions characteristic of the subtypes of cells. PMs are protein found exclusively in certain types or subtypes of cells, or represent particular cell states, RNAMs are genes which demonstrate significant differential expressions between cell types. A total of 109 datasets from four different sources containing sim 120,000 single cells gene expression were used to train and test prediction models. We combined the methods which perform prediction using the whole set of gene features (ANN and Profiles), and those that used specific gene features (PMs and RNAMs) to predict the cell type. The overall classification accuracy was 94.8% for ANN, 94.5% for Profiles, 90.7% for PMs, 67.9% for RNAMs. The combination of four methods showed accuracy of 90.9% with high confidence of positive predictions. The combination of four methods allowed identification of mislabeled cell types in test data sets.en_US
dc.subjectANNen_US
dc.subjectgene markersen_US
dc.subjectPBMCen_US
dc.subjectpredictionen_US
dc.subjectprofilesen_US
dc.subjectprotein markersen_US
dc.subjectscRNA-seqen_US
dc.titlePBMC Cell Classification from Single Cell mRNA Expression by Artificial Neural Networks, Profiles, Gene Markers, and Protein Markersen_US
dc.typeConference Paperen_US
dc.relation.publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021en_US
dc.identifier.doi10.1109/BIBM52615.2021.9669826-
dc.identifier.scopus2-s2.0-85125187788-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85125187788-
dc.contributor.affiliationInformatics and Computer Scienceen_US
dc.relation.firstpage3285en_US
dc.relation.lastpage3290en_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeConference Paper-
crisitem.author.deptInformatics and Computer Science-
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