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DeepProteomics: Protein family classification using Shallow and Deep Networks

Publication Type : Journal Article

Publisher : arXiv preprint arXiv

Source : arXiv preprint arXiv:1809.04461 (2018)

Url :

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2018

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.

Cite this Research Publication : A. Vazhayil, R, V., and Dr. Soman K. P., “DeepProteomics: Protein family classification using Shallow and Deep Networks”, arXiv preprint arXiv:1809.04461, 2018.

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