Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Abstract Emerging evidence indicates the existence of a new class of cancer genes that act as “signal linkers” coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes,...

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Autores principales:	Mehrab Ghanat Bari, Choong Yong Ung, Cheng Zhang, Shizhen Zhu, Hu Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3b591ea664e4688aa46a019ac64179c
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spelling	oai:doaj.org-article:e3b591ea664e4688aa46a019ac64179c2021-12-02T15:05:43ZMachine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks10.1038/s41598-017-07481-52045-2322https://doaj.org/article/e3b591ea664e4688aa46a019ac64179c2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07481-5https://doaj.org/toc/2045-2322Abstract Emerging evidence indicates the existence of a new class of cancer genes that act as “signal linkers” coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.Mehrab Ghanat BariChoong Yong UngCheng ZhangShizhen ZhuHu LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mehrab Ghanat Bari Choong Yong Ung Cheng Zhang Shizhen Zhu Hu Li Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
description	Abstract Emerging evidence indicates the existence of a new class of cancer genes that act as “signal linkers” coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.
format	article
author	Mehrab Ghanat Bari Choong Yong Ung Cheng Zhang Shizhen Zhu Hu Li
author_facet	Mehrab Ghanat Bari Choong Yong Ung Cheng Zhang Shizhen Zhu Hu Li
author_sort	Mehrab Ghanat Bari
title	Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
title_short	Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
title_full	Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
title_fullStr	Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
title_full_unstemmed	Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks
title_sort	machine learning-assisted network inference approach to identify a new class of genes that coordinate the functionality of cancer networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e3b591ea664e4688aa46a019ac64179c
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Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

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