Inferring the Transcriptional Modules Using Penalized Matrix Decomposition

Zheng, Chun-Hou; Zhang, Lei; Ng, To-Yee; Shiu, Simon

doi:10.1007/978-3-642-14932-0_5

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In recent years, many effective mathematical methods have been applied to identify differentially expressed genes. For example, principal component analysis (PCA) [4,5] and penalized matrix decomposition (PMD) [6] have been used to analyze gene expression data. Liu et al used robust principal component analysis (RPCA) to discover differentially expressed genes [7].…”

Section: Introductionmentioning

confidence: 99%

Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

et al. 2017

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Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically,L2,1-norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes.

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Section: Introductionmentioning

confidence: 99%

Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

et al. 2017

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“…Lass et al use the SPCA for clustering and feature selection [3]. In [4], Witten et al proposed a penalized matrix decomposition, which was used to infer the transcriptional modules by Zheng et al [5]. Zhu used a penalized logistic regression to classify the gene data [6].…”

Section: Introductionmentioning

confidence: 99%

Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses

Liu

Zheng

2011

The Fourth International Workshop on Advanced Computational Intelligence

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Sparse methods have a significant advantage of reducing gene expression data complexity to make them comprehensible and interpretable. In this paper, based on Lasso Logistic Regression (LLR), we propose a novel approach to extract plant characteristic gene set, namely coregenes, responding to abiotic stresses. Firstly, to obtain the regression coefficients, the lasso logistic regression was performed according to the samples. Then, the regression coefficients were sorted by the absolute value of them. Finally, the corresponding genes of the nonzero entries of the coefficients are selected as the coregene. Each of coregene extracted can capture the changes of the samples belong to the same condition. The experimental results show that the proposed LLR-based method is efficient to extract the coregenes concerning straight with the stresses.

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Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition

Liu

Zheng

2012

Computers in Biology and Medicine

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Inferring the Transcriptional Modules Using Penalized Matrix Decomposition

Cited by 3 publications

References 11 publications

Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses

Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition

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