Use of SVD-based probit transformation in clustering gene expression profiles
Computational Statistics & Data Analysis
Generalized Power Method for Sparse Principal Component Analysis
The Journal of Machine Learning Research
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
A novel approach for biclustering gene expression data using modular singular value decomposition
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Inferring the transcriptional modules using penalized matrix decomposition
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Discovering the transcriptional modules using microarray data by penalized matrix decomposition
Computers in Biology and Medicine
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Sparse methods have a significant advantage to reduce the complexity of genes expression data and to make them more comprehensible and interpretable. In this paper, based on penalized matrix decomposition (PMD), a novel approach is proposed to extract plants core genes, i.e., the characteristic gene set, responding to abiotic stresses. Core genes can capture the changes of the samples. In other words, the features of samples can be caught by the core genes. The experimental results show that the proposed PMD-based method is efficient to extract the core genes closely related to the abiotic stresses.