Improving protein function prediction using protein sequence and GO-term similarities.

ABSTRACT:

Motivation

Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict.

Results

We compare proteins using a sequence similarity profile (SSP) to a set of annotated training proteins. We introduce two new LSDR methods, one based on the structure of the GO, and

SUBMITTER: Makrodimitris S 

PROVIDER: S-EPMC6449755 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature