ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.

ABSTRACT:

Motivation

Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a framework combining genetic data into pathway structure and using an ensemble of convolutional neural networks (CNNs) along with a Canonical Correlation Regularizer layer for comprehensive prediction of disease risk. The novelty of our approach lies in our two-step framework: (i) utilizing the CNN's effectiveness to extract the complex gene associations within individual genetic pathways and (ii) fusing features from ensemble of CNNs through Canonical Correlation Regularization layer to incorporate the interactions between pathways which

SUBMITTER: Sharma D 

PROVIDER: S-EPMC10666205 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature