Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.

ABSTRACT:

Background

Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction.

Results

We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adju

SUBMITTER: Law JN 

PROVIDER: S-EPMC8716363 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature