  The present gap between the amount of available protein sequence due to the
development of next generation sequencing technology (NGS) and slow and
expensive experimental extraction of useful information like annotation of
protein sequence in different functional aspects, is ever widening, which can
be reduced by employing automatic function prediction (AFP) approaches. Gene
Ontology (GO), comprising of more than 40, 000 classes, defines three aspects
of protein function names Biological Process (BP), Cellular Component (CC),
Molecular Function (MF). Multiple functions of a single protein, has made
automatic function prediction a large-scale, multi-class, multi-label task. In
this paper, we present DEEPGONET, a novel cascaded convolutional and recurrent
neural network, to predict the top-level hierarchy of GO ontology. The network
takes the primary sequence of protein as input which makes it more useful than
other prevailing state-of-the-art deep learning based methods with multi-modal
input, making them less applicable for proteins where only primary sequence is
available. All predictions of different protein functions of our network are
performed by the same architecture, a proof of better generalization as
demonstrated by promising performance on a variety of organisms while trained
on Homo sapiens only, which is made possible by efficient exploration of vast
output space by leveraging hierarchical relationship among GO classes. The
promising performance of our model makes it a potential avenue for directing
experimental protein functions exploration efficiently by vastly eliminating
possible routes which is done by the exploring only the suggested routes from
our model. Our proposed model is also very simple and efficient in terms of
computational time and space compared to other architectures in literature.
