Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
Recent technological advances have introduced new high-throughput methods for
studying host-virus interactions, but testing synergistic interactions between
host gene pairs during infection remains relatively slow and labor intensive.
Identification of multiple gene knockdowns that effectively inhibit viral
replication requires a search over the combinatorial space of all possible
target gene pairs and is infeasible via brute-force experiments. Although
active learning methods for sequential experimental design have shown promise,
existing approaches have generally been restricted to single-gene knockdowns or
small-scale double knockdown datasets. In this study, we present an integrated
Deep Active Learning (DeepAL) framework that incorporates information from a
biological knowledge graph (SPOKE, the Scalable Precision Medicine Open
Knowledge Engine) to efficiently search the configuration space of a large
dataset of all pairwise knockdowns of 356 human genes in HIV infection. Through
graph representation learning, the framework is able to generate task-specific
representations of genes while also balancing the exploration-exploitation
trade-off to pinpoint highly effective double-knockdown pairs. We additionally
present an ensemble method for uncertainty quantification and an interpretation
of the gene pairs selected by our algorithm via pathway analysis. To our
knowledge, this is the first work to show promising results on double-gene
knockdown experimental data of appreciable scale (356 by 356 matrix).
