Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering.

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

SUBMITTER: Liu J 

PROVIDER: S-EPMC5974408 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature