Journal of Computer and Communications

Volume 5, Issue 1 (January 2017)

ISSN Print: 2327-5219   ISSN Online: 2327-5227

Google-based Impact Factor: 1.27  Citations  

Using Neural Networks to Predict Secondary Structure for Protein Folding

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DOI: 10.4236/jcc.2017.51001    1,529 Downloads   2,150 Views   Citations

ABSTRACT

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

Cite this paper

Ibrahim, A. and Yasseen, I. (2017) Using Neural Networks to Predict Secondary Structure for Protein Folding. Journal of Computer and Communications, 5, 1-8. doi: 10.4236/jcc.2017.51001.

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