Network-based machine learning and graph theory algorithms for precision oncology

Abstract Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned dr...

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Autores principales:	Wei Zhang, Jeremy Chien, Jeongsik Yong, Rui Kuang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/1f00df00fc5746258cca79b4f42f264e
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spelling	oai:doaj.org-article:1f00df00fc5746258cca79b4f42f264e2021-12-02T16:05:45ZNetwork-based machine learning and graph theory algorithms for precision oncology10.1038/s41698-017-0029-72397-768Xhttps://doaj.org/article/1f00df00fc5746258cca79b4f42f264e2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41698-017-0029-7https://doaj.org/toc/2397-768XAbstract Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug–disease–gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.Wei ZhangJeremy ChienJeongsik YongRui KuangNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 1, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Wei Zhang Jeremy Chien Jeongsik Yong Rui Kuang Network-based machine learning and graph theory algorithms for precision oncology
description	Abstract Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug–disease–gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.
format	article
author	Wei Zhang Jeremy Chien Jeongsik Yong Rui Kuang
author_facet	Wei Zhang Jeremy Chien Jeongsik Yong Rui Kuang
author_sort	Wei Zhang
title	Network-based machine learning and graph theory algorithms for precision oncology
title_short	Network-based machine learning and graph theory algorithms for precision oncology
title_full	Network-based machine learning and graph theory algorithms for precision oncology
title_fullStr	Network-based machine learning and graph theory algorithms for precision oncology
title_full_unstemmed	Network-based machine learning and graph theory algorithms for precision oncology
title_sort	network-based machine learning and graph theory algorithms for precision oncology
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/1f00df00fc5746258cca79b4f42f264e
work_keys_str_mv	AT weizhang networkbasedmachinelearningandgraphtheoryalgorithmsforprecisiononcology AT jeremychien networkbasedmachinelearningandgraphtheoryalgorithmsforprecisiononcology AT jeongsikyong networkbasedmachinelearningandgraphtheoryalgorithmsforprecisiononcology AT ruikuang networkbasedmachinelearningandgraphtheoryalgorithmsforprecisiononcology
_version_	1718385133811138560

Network-based machine learning and graph theory algorithms for precision oncology

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