Next Article in Journal
Phenolic Content and Antioxidant Activity in Raw and Denatured Aqueous Extracts from Sprouts and Wheatgrass of Einkorn and Emmer Obtained under Salinity
Next Article in Special Issue
A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks
Previous Article in Journal
The Molecular Design of Active Sites in Nanoporous Materials for Sustainable Catalysis
Previous Article in Special Issue
An Interface for Biomedical Big Data Processing on the Tianhe-2 Supercomputer
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Molecules 2017, 22(12), 2131;

A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering

School of Information Science and Engineering, Central South University, Changsha 410083, China
School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Revised: 27 November 2017 / Accepted: 29 November 2017 / Published: 2 December 2017
Full-Text   |   PDF [1541 KB, uploaded 4 December 2017]   |  


Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data. However, the NMF-based method is performed within the Euclidean space, and it is usually inappropriate for revealing the intrinsic geometric structure of data space. In order to overcome this shortcoming, Cai et al. proposed a novel algorithm, called graph regularized non-negative matrices factorization (GNMF). Motivated by the topological structure of the GNMF-based method, we propose improved graph regularized non-negative matrix factorization (GNMF) to facilitate the display of geometric structure of data space. Robust manifold non-negative matrix factorization (RM-GNMF) is designed for cancer gene clustering, leading to an enhancement of the GNMF-based algorithm in terms of robustness. We combine the l 2 , 1 -norm NMF with spectral clustering to conduct the wide-ranging experiments on the three known datasets. Clustering results indicate that the proposed method outperforms the previous methods, which displays the latest application of the RM-GNMF-based method in cancer gene clustering. View Full-Text
Keywords: robust; manifold; matrix factorization; gene clustering robust; manifold; matrix factorization; gene clustering

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhu, R.; Liu, J.-X.; Zhang, Y.-K.; Guo, Y. A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering. Molecules 2017, 22, 2131.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top