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作 者:Ali Abdulhafidh Ibrahim Ibrahim Sabah Yasseen
机构地区:[1]College of Science, Al-Nahrain University, Baghdad, Iraq [2]College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
出 处:《Journal of Computer and Communications》2017年第1期1-8,共8页电脑和通信(英文)
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.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.
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)
分 类 号:TP39[自动化与计算机技术—计算机应用技术]
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