  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.
