Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning

Abstract Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to i...

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Autores principales:	Ali Reza Ebadi, Ali Soleimani, Abdulbaghi Ghaderzadeh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/302fafc3054648acbe50e49650d61be5
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spelling	oai:doaj.org-article:302fafc3054648acbe50e49650d61be52021-12-02T14:53:43ZProviding an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning10.1038/s41598-021-88548-22045-2322https://doaj.org/article/302fafc3054648acbe50e49650d61be52021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88548-2https://doaj.org/toc/2045-2322Abstract Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more to predict the driver genes and subgroups of driver genes which have the highest degree of overlap due to p-value with known driver genes in valid databases. Driver genes are the benchmark of MsigDB which have more overlap compared to them as selected driver genes. In this article, in addition to including the driver genes defined in previous work, introduce newer driver genes. The minister will define newer groups of driver genes compared to other methods the p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. Due to the overlap and newer driver genes and driver gene group and subgroups. The results show that the p value of the proposed method is about 2.7 times less than the driver sub method due to overlap, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.Ali Reza EbadiAli SoleimaniAbdulbaghi GhaderzadehNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ali Reza Ebadi Ali Soleimani Abdulbaghi Ghaderzadeh Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
description	Abstract Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more to predict the driver genes and subgroups of driver genes which have the highest degree of overlap due to p-value with known driver genes in valid databases. Driver genes are the benchmark of MsigDB which have more overlap compared to them as selected driver genes. In this article, in addition to including the driver genes defined in previous work, introduce newer driver genes. The minister will define newer groups of driver genes compared to other methods the p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. Due to the overlap and newer driver genes and driver gene group and subgroups. The results show that the p value of the proposed method is about 2.7 times less than the driver sub method due to overlap, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.
format	article
author	Ali Reza Ebadi Ali Soleimani Abdulbaghi Ghaderzadeh
author_facet	Ali Reza Ebadi Ali Soleimani Abdulbaghi Ghaderzadeh
author_sort	Ali Reza Ebadi
title	Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
title_short	Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
title_full	Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
title_fullStr	Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
title_full_unstemmed	Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
title_sort	providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/302fafc3054648acbe50e49650d61be5
work_keys_str_mv	AT alirezaebadi providinganoptimizedmodeltodetectdrivergenesfromheterogeneouscancersamplesusingrestrictioninsubspacelearning AT alisoleimani providinganoptimizedmodeltodetectdrivergenesfromheterogeneouscancersamplesusingrestrictioninsubspacelearning AT abdulbaghighaderzadeh providinganoptimizedmodeltodetectdrivergenesfromheterogeneouscancersamplesusingrestrictioninsubspacelearning
_version_	1718389374763139072

Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning

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