Cargando…
Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms
Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors....
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141966/ https://www.ncbi.nlm.nih.gov/pubmed/35627101 http://dx.doi.org/10.3390/genes13050716 |
_version_ | 1784715471879143424 |
---|---|
author | Li, Feng Chu, Xin Dai, Lingyun Wang, Juan Liu, Jinxing Shang, Junliang |
author_facet | Li, Feng Chu, Xin Dai, Lingyun Wang, Juan Liu, Jinxing Shang, Junliang |
author_sort | Li, Feng |
collection | PubMed |
description | Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors. In this study, we present a framework to analyze the effects of multi-omics characteristics on the identification of driver genes. We utilize four machine learning algorithms within this framework to detect cancer driver genes in pan-cancer data, including 75 characteristics among 19,636 genes. The 75 features are divided into four types and analyzed using Kullback–Leibler divergence based on CGC genes and non-CGC genes. We detect cancer driver genes in two different ways. One is to detect driver genes from a single feature type, while the other is from the top N features. The first analysis denotes that the mutational features are the best characteristics. The second analysis reveals that the top 45 features are the most effective feature combinations and superior to the mutational features. The top 45 features not only contain mutational features but also three other types of features. Therefore, our study extends the detection of cancer driver genes and provides a more comprehensive understanding of cancer mechanisms. |
format | Online Article Text |
id | pubmed-9141966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91419662022-05-28 Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms Li, Feng Chu, Xin Dai, Lingyun Wang, Juan Liu, Jinxing Shang, Junliang Genes (Basel) Article Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors. In this study, we present a framework to analyze the effects of multi-omics characteristics on the identification of driver genes. We utilize four machine learning algorithms within this framework to detect cancer driver genes in pan-cancer data, including 75 characteristics among 19,636 genes. The 75 features are divided into four types and analyzed using Kullback–Leibler divergence based on CGC genes and non-CGC genes. We detect cancer driver genes in two different ways. One is to detect driver genes from a single feature type, while the other is from the top N features. The first analysis denotes that the mutational features are the best characteristics. The second analysis reveals that the top 45 features are the most effective feature combinations and superior to the mutational features. The top 45 features not only contain mutational features but also three other types of features. Therefore, our study extends the detection of cancer driver genes and provides a more comprehensive understanding of cancer mechanisms. MDPI 2022-04-19 /pmc/articles/PMC9141966/ /pubmed/35627101 http://dx.doi.org/10.3390/genes13050716 Text en © 2022 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 Li, Feng Chu, Xin Dai, Lingyun Wang, Juan Liu, Jinxing Shang, Junliang Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title | Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title_full | Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title_fullStr | Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title_full_unstemmed | Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title_short | Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms |
title_sort | effects of multi-omics characteristics on identification of driver genes using machine learning algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141966/ https://www.ncbi.nlm.nih.gov/pubmed/35627101 http://dx.doi.org/10.3390/genes13050716 |
work_keys_str_mv | AT lifeng effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms AT chuxin effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms AT dailingyun effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms AT wangjuan effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms AT liujinxing effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms AT shangjunliang effectsofmultiomicscharacteristicsonidentificationofdrivergenesusingmachinelearningalgorithms |