By using MADALINE Learning with Back Propagation and Keras to Predict the Protein Secondary Structure
Shivani Agarwal1, Pankaj Agarwal2
1Shivani Agarwal, Assistant professor, Computer Science & Engineering Department, IMS Engineering College, Ghaziabad ,India.
2Pankaj Agarwal, Head, Computer Science & Engineering Department, IMS Engineering College, Ghaziabad, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 4878-4882 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4964129219/2019©BEIESP | DOI: 10.35940/ijeat.B4964.129219
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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: Understanding of intermediate protein structure prediction serves as a crucial component to find the function of residues of amino acid. In this paper, focus on the intermediate protein structure by using feed forward and feedback method and enhancing the concept of sliding window. Prediction of secondary structure is a very cosmic problem of bioinformatics. This can be reduced by predicting or unfold the protein structures if it is unfolded so that can give the great results in medical sciences. Our main motive is to improve the accuracy of secondary structures and minimize the error .Experimentally, use the Multilayer ADALINE network for learning and KERAS TENSORFLOW use for train the weight matrix and sigmoid function for calculating the resultant with back propagation. Resultant of this paper results provides more prominent results as compare to already existing methods. Those improve the accuracy of secondary structure prediction.
Keywords: Residue, Multilayer ADALINE learning, KERAS TENSORFLOW, Back propagation, Improve accuracy,Sliding Window.