Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery.

ABSTRACT: In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompass

SUBMITTER: Soper B 

PROVIDER: S-EPMC12379194 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature