HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

ABSTRACT: The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by grap

SUBMITTER: Huang Y 

PROVIDER: S-EPMC9909422 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature