Protein Secondary Structure Prediction with Gated Recurrent Neural Networks
R.Thendral1, AN.Sigappi2
1Thendral R*, Research Scholar, Computer Science and Engineering, Annamalai University, India.
2Dr. Sigappi.AN, Professor, Computer Science Engineering, Annamalai University, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on 27 November, 2019. | Manuscript published on December 10, 2019. | PP: 3915-3918 | Volume-9 Issue-2, December 2019. | Retrieval Number: A4546119119/2019©BEIESP | DOI: 10.35940/ijitee.A4546.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 .
Keywords: Protein Structure Prediction, Recurrent Neural Network, Long Short Term Memory.
Scope of the Article: Concrete Structures