Functional protein representations from biological networks enable diverse cross-species inference.

ABSTRACT: Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins

SUBMITTER: Fan J 

PROVIDER: S-EPMC6511848 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature