Open AccessProtein Secondary Structure Prediction with Gated Recurrent Neural Networks
Author(s) -
Thendral R*,
Seongpil An
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a4546.129219
Subject(s) - protein structure prediction , artificial neural network , computer science , recurrent neural network , artificial intelligence , sequence (biology) , protein secondary structure , feature (linguistics) , deep learning , machine learning , data set , set (abstract data type) , protein structure , protein sequencing , protein data bank (rcsb pdb) , pattern recognition (psychology) , data mining , algorithm , peptide sequence , biology , biochemistry , linguistics , genetics , philosophy , gene , programming language
In computational biology, the protein structure from its amino acid sequence is difficult to predict, which impact the design of drug and molecular biology. Improving the accuracy of predicting acceptable protein structure is the main problem of predicting structure problem. The deep learning method is suitable for high level relation feature from the target protein sequence. Recurrent Neural Network(RNN) handle sequence data in effective manner. Experiment conducted on a well-known standard data set of the RCSB[12] shows that our model is extensively better than the state-of-the-art methods in different statistical measurement. This study makes clear and carry out the deep learning method can increase the protein properties and achieve a Q3 accuracy of 86 percentages .