LARGE-SCALE MULTIPLE INFERENCE OF COLLECTIVE DEPENDENCE WITH APPLICATIONS TO PROTEIN FUNCTION.

ABSTRACT: Measuring the dependence of k ≥ 3 random variables and drawing inference from such higher-order dependences are scientifically important yet challenging. Motivated here by protein coevolution with multivariate categorical features, we consider an information theoretic measure of higher-order dependence. The proposed collective dependence is a symmetrization of differential interaction information which generalizes the mutual information of a pair of random variables. We show that the collective dependence can be easily estimated and facilitates a test on the dependence of k ≥ 3 random variables. Upon carefully exploring the null space of collective dependence, we devise a Classification-Assisted Large scaLe inference procedure to DEtect sign

SUBMITTER: Jernigan R 

PROVIDER: S-EPMC9337751 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature