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Rao, P.N., Devi, T.U., Kaladhar, D., Sridhar, G. and Rao, A.A. (2009) A Probabilistic Neural Network Approach for Protein Superfamily Classification. Journal of Theoretical and Applied Information Technology, 6, 101-105.

has been cited by the following article:

• TITLE: Using Neural Networks to Predict Secondary Structure for Protein Folding

  AUTHORS: Ali Abdulhafidh Ibrahim, Ibrahim Sabah Yasseen

  KEYWORDS: Protein Secondary Structure Prediction (PSSP), Neural Network (NN), α-Helix (H), β-Sheet (E), Coil (C), Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN)

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.1, December 29, 2016

  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.