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Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data
Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes fr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467493/ https://www.ncbi.nlm.nih.gov/pubmed/34575089 http://dx.doi.org/10.3390/life11090940 |
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author | Huang, Guo-Hua Zhang, Yu-Hang Chen, Lei Li, You Huang, Tao Cai, Yu-Dong |
author_facet | Huang, Guo-Hua Zhang, Yu-Hang Chen, Lei Li, You Huang, Tao Cai, Yu-Dong |
author_sort | Huang, Guo-Hua |
collection | PubMed |
description | Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew’s correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study. |
format | Online Article Text |
id | pubmed-8467493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84674932021-09-27 Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data Huang, Guo-Hua Zhang, Yu-Hang Chen, Lei Li, You Huang, Tao Cai, Yu-Dong Life (Basel) Article Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew’s correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study. MDPI 2021-09-09 /pmc/articles/PMC8467493/ /pubmed/34575089 http://dx.doi.org/10.3390/life11090940 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Guo-Hua Zhang, Yu-Hang Chen, Lei Li, You Huang, Tao Cai, Yu-Dong Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title | Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title_full | Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title_fullStr | Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title_full_unstemmed | Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title_short | Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data |
title_sort | identifying lung cancer cell markers with machine learning methods and single-cell rna-seq data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467493/ https://www.ncbi.nlm.nih.gov/pubmed/34575089 http://dx.doi.org/10.3390/life11090940 |
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