research-article

Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization

Authors:

Chun-Hou Zheng,

Yong XuAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 13, Issue 6

Pages 1059 - 1067

https://doi.org/10.1109/TCBB.2015.2505294

Published: 01 November 2016 Publication History

Abstract

Many methods have been considered for gene selection and analysis of gene expression data. Nonetheless, there still exists the considerable space for improving the explicitness and reliability of gene selection. To this end, this paper proposes a novel method named robust graph regularized non-negative matrix factorization for characteristic gene selection using gene expression data, which mainly contains two aspects: Firstly, enforcing L₂₁-norm minimization on error function which is robust to outliers and noises in data points. Secondly, it considers that the samples lie in low-dimensional manifold which embeds in a high-dimensional ambient space, and reveals the data geometric structure embedded in the original data. To demonstrate the validity of the proposed method, we apply it to gene expression data sets involving various human normal and tumor tissue samples and the results demonstrate that the method is effective and feasible.

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Cited By

Yang LYan LWei XYang X(2023)Label consistency-based deep semisupervised NMF for tumor recognitionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105511117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105511
Ram PKuila P(2023)Dynamic scaling factor based differential evolution with multi-layer perceptron for gene selection from pathway information of microarray dataMultimedia Tools and Applications10.1007/s11042-022-13964-z82:9(13453-13478)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13964-z
Nasiri EBerahmand KLi Y(2023)Robust graph regularization nonnegative matrix factorization for link prediction in attributed networksMultimedia Tools and Applications10.1007/s11042-022-12943-882:3(3745-3768)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-022-12943-8
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Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization
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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 13, Issue 6

November 2016

198 pages

ISSN:1545-5963

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Copyright © 2016.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 November 2016

Published in TCBB Volume 13, Issue 6

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Cited By

Yang LYan LWei XYang X(2023)Label consistency-based deep semisupervised NMF for tumor recognitionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105511117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105511
Ram PKuila P(2023)Dynamic scaling factor based differential evolution with multi-layer perceptron for gene selection from pathway information of microarray dataMultimedia Tools and Applications10.1007/s11042-022-13964-z82:9(13453-13478)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13964-z
Nasiri EBerahmand KLi Y(2023)Robust graph regularization nonnegative matrix factorization for link prediction in attributed networksMultimedia Tools and Applications10.1007/s11042-022-12943-882:3(3745-3768)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-022-12943-8
Chen KChe HLi XLeung M(2023)Graph non-negative matrix factorization with alternative smoothed regularizationsNeural Computing and Applications10.1007/s00521-022-07200-w35:14(9995-10009)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00521-022-07200-w
Liu JWang DGao YZheng CXu YYu J(2018)Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering SamplesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2017.266555715:3(974-987)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TCBB.2017.2665557
Hou MLiu JShang JGao YKong XDai L(2018)Performance Analysis of Non-negative Matrix Factorization Methods on TCGA DataIntelligent Computing Theories and Application10.1007/978-3-319-95933-7_50(407-418)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1007/978-3-319-95933-7_50

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