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Integrated Robust Structured NMF Model for Sample Clustering and Network Analysis

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  • 1. School of Computer, Qufu Normal University, Rizhao 276826, Shandong, China;
    2. School of Information, Beijing Forestry University, Beijing 100083, China

Received date: 2020-06-15

  Online published: 2020-10-14

Abstract

In order to preserve the homogeneity among data more effectively, this paper proposes an integrated robust structured non-negative matrix factorization (integrated robust structured non-negative matrix factorization, iRSNMF) model with an induced structured term. We verify the effectiveness of this model by applying it to the clustering experiments of cancer samples and the analysis of gene co-expression network. Reasonable biological explanations of related genes and pathways are given based on existing literature. Experimental results show that the iRSNMF method has excellent clustering performance and more-key genes mining ability. The genes and pathways obtained by the iRSNMF model play an important role in cancer pathogenesis, accordingly, providing a new idea for the diagnosis, treatment and prognosis of cancer.

Cite this article

ZHANG Xiaoning, KONG Xiangzhen, LUO Chuanwen, LIU Jinxing . Integrated Robust Structured NMF Model for Sample Clustering and Network Analysis[J]. Journal of Applied Sciences, 2020 , 38(5) : 825 -842 . DOI: 10.3969/j.issn.0255-8297.2020.05.012

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