An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been...

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Autores principales:	Xia Chen, Yexiong Lin, Qiang Qu, Bin Ning, Haowen Chen, Xiong Li
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	genetic algorithm bayesian network information entropy tumor subtype classification genome variation Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/9a26eafa31734643a30c0ab3ee83fe35
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spelling	oai:doaj.org-article:9a26eafa31734643a30c0ab3ee83fe352021-11-23T02:34:07ZAn epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria10.3934/mbe.20213821551-0018https://doaj.org/article/9a26eafa31734643a30c0ab3ee83fe352021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021382?viewType=HTMLhttps://doaj.org/toc/1551-0018Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been some computational methods to study tumor heterogeneity from the level of genome, transcriptome, and histology, but these methods still have certain limitations. In this study, we proposed an epistasis and heterogeneity analysis method based on genomic single nucleotide polymorphism (SNP) data. First of all, a maximum correlation and maximum consistence criteria was designed based on Bayesian network score K2 and information entropy for evaluating genomic epistasis. As the number of SNPs increases, the epistasis combination space increases sharply, resulting in a combination explosion phenomenon. Therefore, we next use an improved genetic algorithm to search the SNP epistatic combination space for identifying potential feasible epistasis solutions. Multiple epistasis solutions represent different pathogenic gene combinations, which may lead to different tumor subtypes, that is, heterogeneity. Finally, the XGBoost classifier is trained with feature SNPs selected that constitute multiple sets of epistatic solutions to verify that considering tumor heterogeneity is beneficial to improve the accuracy of tumor subtype prediction. In order to demonstrate the effectiveness of our method, the power of multiple epistatic recognition and the accuracy of tumor subtype classification measures are evaluated. Extensive simulation results show that our method has better power and prediction accuracy than previous methods.Xia ChenYexiong LinQiang Qu Bin NingHaowen ChenXiong LiAIMS Pressarticlegenetic algorithmbayesian networkinformation entropytumor subtype classificationgenome variationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7711-7726 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic algorithm bayesian network information entropy tumor subtype classification genome variation Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	genetic algorithm bayesian network information entropy tumor subtype classification genome variation Biotechnology TP248.13-248.65 Mathematics QA1-939 Xia Chen Yexiong Lin Qiang Qu Bin Ning Haowen Chen Xiong Li An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
description	Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been some computational methods to study tumor heterogeneity from the level of genome, transcriptome, and histology, but these methods still have certain limitations. In this study, we proposed an epistasis and heterogeneity analysis method based on genomic single nucleotide polymorphism (SNP) data. First of all, a maximum correlation and maximum consistence criteria was designed based on Bayesian network score K2 and information entropy for evaluating genomic epistasis. As the number of SNPs increases, the epistasis combination space increases sharply, resulting in a combination explosion phenomenon. Therefore, we next use an improved genetic algorithm to search the SNP epistatic combination space for identifying potential feasible epistasis solutions. Multiple epistasis solutions represent different pathogenic gene combinations, which may lead to different tumor subtypes, that is, heterogeneity. Finally, the XGBoost classifier is trained with feature SNPs selected that constitute multiple sets of epistatic solutions to verify that considering tumor heterogeneity is beneficial to improve the accuracy of tumor subtype prediction. In order to demonstrate the effectiveness of our method, the power of multiple epistatic recognition and the accuracy of tumor subtype classification measures are evaluated. Extensive simulation results show that our method has better power and prediction accuracy than previous methods.
format	article
author	Xia Chen Yexiong Lin Qiang Qu Bin Ning Haowen Chen Xiong Li
author_facet	Xia Chen Yexiong Lin Qiang Qu Bin Ning Haowen Chen Xiong Li
author_sort	Xia Chen
title	An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
title_short	An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
title_full	An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
title_fullStr	An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
title_full_unstemmed	An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
title_sort	epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/9a26eafa31734643a30c0ab3ee83fe35
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_version_	1718417391183986688

An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

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