Title: DeepProteomics: Protein family classification using Shallow and Deep Networks,
Abstract: The knowledge regarding the function of proteins is necessary as it gives a
clear picture of biological processes. Nevertheless, there are many protein
sequences found and added to the databases but lacks functional annotation. The
laboratory experiments take a considerable amount of time for annotation of the
sequences. This arises the need to use computational techniques to classify
proteins based on their functions. In our work, we have collected the data from
Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass
it to recurrent neural network(RNN), long short term memory(LSTM) and gated
recurrent unit(GRU) model and compare it by applying trigram with deep neural
network and shallow neural network on the same dataset. Through this approach,
we could achieve maximum of around 78% accuracy for the classification of
protein families.