DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
This work addresses gene function prediction for non-model organisms like potato, offering a scalable method but is incremental as it builds on existing deep learning and network approaches.
The paper tackled the problem of predicting gene functions in non-model organisms by fusing systems-level information with temporal dynamics using deep learning, achieving reliable classification into five functional ontology categories for potato genes.
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.