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

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: it outperformed all other FS measures by >5-10% on yeast and human reference datasets on protein-protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism.

Automated summary

This article introduces a deep learning method called deepSimDEF for automatically learning functional similarity estimation of gene pairs. This method calculates the functional similarity by learning the embedding vector representations of GO annotations and gene products. The results show that deepSimDEF outperforms other methods on tasks related to protein-protein interactions, gene co-expression, and sequence homology.