A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

ABSTRACT: Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predi

SUBMITTER: Zhan L 

PROVIDER: S-EPMC11370636 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature