Using Neural Networks to Predict Secondary Structure for Protein Folding

• 作者: Ali Abdulhafidh Ibrahim Ibrahim Sabah Yasseen
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: 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)
• 年，卷（期）: dnhtxyw_2017,（1）
• 页码范围: 1-8
• 页数: 8页
• 分类号: TP39

摘要

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.