  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 that 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 (RNAi), clustered regularly interspaced short palindromic
repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than
existing approaches in predicting inducible gene expression changes for all
three types of genetic perturbations. TranscriptionNet can predict
transcriptional profiles for all genes in existing biological networks and
increases perturbational gene expression changes for each type of genetic
perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates
strong generalization ability when comparing predicted and true gene expression
changes on different external tasks. Overall, TranscriptionNet can systemically
predict transcriptional consequences induced by perturbing genes on a
genome-wide scale and thus holds promise to systemically detect gene function
and enhance drug development and target discovery.
