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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...

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Detalles Bibliográficos
Autores principales: Huang, Guo-Hua, Zhang, Yu-Hang, Chen, Lei, Li, You, Huang, Tao, Cai, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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