Network-aided Bi-Clustering for discovering cancer subtypes

Bi-clustering is a widely used data mining technique for analyzing gene expression data. It simultaneously groups genes and samples of an input gene expression data matrix to discover bi-clusters that relevant samples exhibit similar gene expression profiles over a subset of genes. The discovered bi...

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Autores principales:	Guoxian Yu, Xianxue Yu, Jun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3dbfc42c20649daa8fcb3b3d785b465
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spelling	oai:doaj.org-article:e3dbfc42c20649daa8fcb3b3d785b4652021-12-02T15:06:01ZNetwork-aided Bi-Clustering for discovering cancer subtypes10.1038/s41598-017-01064-02045-2322https://doaj.org/article/e3dbfc42c20649daa8fcb3b3d785b4652017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01064-0https://doaj.org/toc/2045-2322Bi-clustering is a widely used data mining technique for analyzing gene expression data. It simultaneously groups genes and samples of an input gene expression data matrix to discover bi-clusters that relevant samples exhibit similar gene expression profiles over a subset of genes. The discovered bi-clusters bring insights for categorization of cancer subtypes, gene treatments and others. Most existing bi-clustering approaches can only enumerate bi-clusters with constant values. Gene interaction networks can help to understand the pattern of cancer subtypes, but they are rarely integrated with gene expression data for exploring cancer subtypes. In this paper, we propose a novel method called Network-aided Bi-Clustering (NetBC). NetBC assigns weights to genes based on the structure of gene interaction network, and it iteratively optimizes sum-squared residue to obtain the row and column indicative matrices of bi-clusters by matrix factorization. NetBC can not only efficiently discover bi-clusters with constant values, but also bi-clusters with coherent trends. Empirical study on large-scale cancer gene expression datasets demonstrates that NetBC can more accurately discover cancer subtypes than other related algorithms.Guoxian YuXianxue YuJun WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guoxian Yu Xianxue Yu Jun Wang Network-aided Bi-Clustering for discovering cancer subtypes
description	Bi-clustering is a widely used data mining technique for analyzing gene expression data. It simultaneously groups genes and samples of an input gene expression data matrix to discover bi-clusters that relevant samples exhibit similar gene expression profiles over a subset of genes. The discovered bi-clusters bring insights for categorization of cancer subtypes, gene treatments and others. Most existing bi-clustering approaches can only enumerate bi-clusters with constant values. Gene interaction networks can help to understand the pattern of cancer subtypes, but they are rarely integrated with gene expression data for exploring cancer subtypes. In this paper, we propose a novel method called Network-aided Bi-Clustering (NetBC). NetBC assigns weights to genes based on the structure of gene interaction network, and it iteratively optimizes sum-squared residue to obtain the row and column indicative matrices of bi-clusters by matrix factorization. NetBC can not only efficiently discover bi-clusters with constant values, but also bi-clusters with coherent trends. Empirical study on large-scale cancer gene expression datasets demonstrates that NetBC can more accurately discover cancer subtypes than other related algorithms.
format	article
author	Guoxian Yu Xianxue Yu Jun Wang
author_facet	Guoxian Yu Xianxue Yu Jun Wang
author_sort	Guoxian Yu
title	Network-aided Bi-Clustering for discovering cancer subtypes
title_short	Network-aided Bi-Clustering for discovering cancer subtypes
title_full	Network-aided Bi-Clustering for discovering cancer subtypes
title_fullStr	Network-aided Bi-Clustering for discovering cancer subtypes
title_full_unstemmed	Network-aided Bi-Clustering for discovering cancer subtypes
title_sort	network-aided bi-clustering for discovering cancer subtypes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e3dbfc42c20649daa8fcb3b3d785b465
work_keys_str_mv	AT guoxianyu networkaidedbiclusteringfordiscoveringcancersubtypes AT xianxueyu networkaidedbiclusteringfordiscoveringcancersubtypes AT junwang networkaidedbiclusteringfordiscoveringcancersubtypes
_version_	1718388615884570624

Network-aided Bi-Clustering for discovering cancer subtypes

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