ForgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction.

ABSTRACT:

Motivation

A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection.

Results

To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feed

SUBMITTER: Kong Y 

PROVIDER: S-EPMC7267822 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature