Combining physico-chemical properties with PSSM for protein secondary structure prediction using BP neural network

被引：0
|
作者：
Yang, Huiyun [1 ]
Shi, Ouyan [1 ]
Tian, Xin [1 ]
机构：
[1] Tianjin Med Univ, Tianjin 300070, Peoples R China
来源：
BMEI 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOL 1 | 2008年
关键词：
D O I：
10.1109/BMEI.2008.90
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
A two-stage neural network has been used to predict protein secondary structure based on the method of combining physico-chemical properties of amino acid residues with evolutionary information. 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 7-fold cross-validation. The result indicated that the prediction accuracy reached 75.96% which was 0.5% higher than that of only using PSSM as input. Although Q(H) was found to be lower than that of PSSM, C-H had an improvement, which indicates that the method we developed is successful.
引用
收藏
页码：107 / 110
页数：4
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