ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways.

ABSTRACT: Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome

SUBMITTER: Huang X 

PROVIDER: S-EPMC8557386 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature