A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.

ABSTRACT:

Motivation

Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks.

Results

To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to

SUBMITTER: Kong Y 

PROVIDER: S-EPMC6198851 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature