Publication Type : Journal Article
Publisher : arXiv preprint arXiv
Source : arXiv preprint arXiv:1809.04461 (2018)
Url : https://www.researchgate.net/publication/327621216_DeepProteomics_Protein_family_classification_using_Shallow_and_Deep_Networks
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.