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Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering by Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Wang, Z. Jane is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering about?

## Abstract Identification of characteristic genes associated with specific biological processes of different cancers could provide insights into the underlying cancer genetics and cancer prognostic assessment. It is of critical importance to select such characteristic genes effectively. In this paper, a novel unsupervised characteristic gene selection method based on sample learning and sparse filtering, Sample Learning based on Deep Sparse Filtering (SLDSF), is proposed. With sample learning, the proposed SLDSF can better represent the gene expression level by the transformed sample space. Most unsupervised characteristic gene selection methods did not consider deep structures, while a multilayer structure may learn more meaningful representations than a single layer, therefore deep sparse filtering is investigated here to implement sample learning in the proposed SLDSF. Experimental studies on several microarray and RNA-Seq datasets demonstrate that the proposed SLDSF is more effective than several representative characteristic gene selection methods (e.g., RGNMF, GNMF, RPCA and PMD) for selecting cancer characteristic genes.

Who reads Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering?

Author

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Wang, Z. Jane

Publisher

Springer Science and Business Media LLC; London: Nature Publishing Group; [London]: Springer Nature; Nature Publishing Group; Society for Mining, Metallurgy and Exploration Inc.; Research Square; Springer Nature; Island Press (ISSN 2045-2322)

Published

2018

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)