DeepSimDEF: deep neural embeddings of gene products and gene ontology terms for functional analysis of genes.

ABSTRACT:

Motivation

There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein-protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations.

Results

We introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF's key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations

SUBMITTER: Pesaranghader A 

PROVIDER: S-EPMC9154256 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature