Combining Hydrophobicity with PSSM for Improving Prediction Accuracy of α-heliz Using BP Neural Network
A two-stage neural network has been used to predict protein secondary structure based on the method of combining hydrophobicity of amino acid residues with PSSM. We employed CB513 as the dataset. After excluding the protein chains containing X、 B and which with sequence length shorter than 30 amino acids, there were 492 protein chains in this dataset totally. The network has been trained and tested by 4-fold crossvalidation. The result indicated that -helix has been predicted with an averaged accuracy of nearly 79％, sensitivity of 79％ and specificity of 91％. The total prediction accuracy of secondary structure reached 75.96％, which is higher than that of only using PSSM as input.
secondary structure prediction BP neural network hydrophobicity PSSM
Huiyun Yang Ouyan Shi Xin Tian
Department of Biomedical Engineering Tianjin Medical University Tianjin,China
国际会议
上海
英文
271-274
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）