Please use this identifier to cite or link to this item:
https://research.matf.bg.ac.rs/handle/123456789/334
DC Field | Value | Language |
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dc.contributor.author | Veljkovic, Aleksandar | en_US |
dc.contributor.author | Maljkovic, Mirjana | en_US |
dc.contributor.author | Mitić, Nenad | en_US |
dc.contributor.author | Malkov, Saša | en_US |
dc.contributor.author | Lyu, Minjie | en_US |
dc.contributor.author | Lin, Xin | en_US |
dc.contributor.author | Michalewicz, Marek | en_US |
dc.contributor.author | Zhang, Guanglan | en_US |
dc.contributor.author | Brusic, Vladimir | en_US |
dc.date.accessioned | 2022-08-09T12:51:46Z | - |
dc.date.available | 2022-08-09T12:51:46Z | - |
dc.date.issued | 2021-01-01 | - |
dc.identifier.isbn | 9781665401265 | - |
dc.identifier.uri | https://research.matf.bg.ac.rs/handle/123456789/334 | - |
dc.description.abstract | Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set (sim 120,000 PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97% (PBMC separation), 97-100% (magnetic-activated cell sorting), and 82-99% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data. | en_US |
dc.subject | 10x SCT | en_US |
dc.subject | classification | en_US |
dc.subject | data mining | en_US |
dc.subject | dimensionality reduction | en_US |
dc.subject | gene expression | en_US |
dc.subject | machine learning | en_US |
dc.subject | PBMC | en_US |
dc.subject | transcriptome | en_US |
dc.title | Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods | en_US |
dc.type | Conference Paper | en_US |
dc.relation.publication | Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 | en_US |
dc.identifier.doi | 10.1109/BIBM52615.2021.9669844 | - |
dc.identifier.scopus | 2-s2.0-85125205373 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85125205373 | - |
dc.contributor.affiliation | Informatics and Computer Science | en_US |
dc.contributor.affiliation | Informatics and Computer Science | en_US |
dc.relation.firstpage | 3322 | en_US |
dc.relation.lastpage | 3326 | en_US |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairetype | Conference Paper | - |
crisitem.author.dept | Informatics and Computer Science | - |
crisitem.author.dept | Informatics and Computer Science | - |
crisitem.author.orcid | 0000-0002-4385-6322 | - |
Appears in Collections: | Research outputs |
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