DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks

TL;DR

DDeMON leverages deep learning on dynamic multiplex biological networks to predict gene functions in potato, integrating multi-level and temporal data for scalable, accurate annotations validated by protein domain analysis.

Contribution

This work introduces DDeMON, a novel deep learning framework that combines temporal multiplex networks and ontology data for scalable gene function prediction in non-model organisms.

Findings

Successfully predicted novel gene functions in potato.

Validated predictions through protein domain searches.

Capable of handling billions of potential gene links.

Abstract

Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels. The goal of this work is to explore how the fusion of systems' level information with temporal dynamics of gene expression can be used in combination with non-linear approximation power of deep neural networks to predict novel gene functions in a non-model organism potato \emph{Solanum tuberosum}. We propose DDeMON (Dynamic Deep learning from temporal Multiplex Ontology-annotated Networks), an approach for scalable, systems-level inference of function annotation using time-dependent multiscale biological information. The proposed method, which is capable of considering billions of potential links between the genes of interest, was applied on experimental gene expression data and the background knowledge network to reliably classify genes with unknown function into five different functional ontology categories, linked to the experimental data set. Predicted novel functions of genes were validated using extensive protein domain search approach.