Combining Physico-chemical Properties with PSSM for Protein Secondary Structure Prediction Using BP Neural Network

May 27-May 30

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http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.90

2008 International Conference on BioM ...

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	Huiyun Yang, Ouyan Shi, Xin Tian, "Combining Physico-chemical Properties with PSSM for Protein Secondary Structure Prediction Using BP Neural Network," BioMedical Engineering and Informatics, International Conference on, vol. 1, pp. 107-110, 2008 International Conference on BioMedical Engineering and Informatics, 2008.

	BibTex	x
	@article{ 10.1109/BMEI.2008.90, author = {Huiyun Yang and Ouyan Shi and Xin Tian}, title = {Combining Physico-chemical Properties with PSSM for Protein Secondary Structure Prediction Using BP Neural Network}, journal ={BioMedical Engineering and Informatics, International Conference on}, volume = {1}, year = {2008}, isbn = {978-0-7695-3118-2}, pages = {107-110}, doi = {http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.90}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - BioMedical Engineering and Informatics, International Conference on TI - Combining Physico-chemical Properties with PSSM for Protein Secondary Structure Prediction Using BP Neural Network SN - 978-0-7695-3118-2 SP107 EP110 A1 - Huiyun Yang, A1 - Ouyan Shi, A1 - Xin Tian, PY - 2008 KW - protein secondary structure KW - prediction KW - BP neural network KW - hydrophobicity KW - isoelectric point KW - PSSM VL - 1 JA - BioMedical Engineering and Informatics, International Conference on ER -

Huiyun Yang

Ouyan Shi

Xin Tian

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 QH was found to be lower than that of PSSM, CH had an improvement, which indicates that the method we developed is successful.

Index Terms:

protein secondary structure, prediction, BP neural network, hydrophobicity, isoelectric point, PSSM

Citation:

Huiyun Yang, Ouyan Shi, Xin Tian, "Combining Physico-chemical Properties with PSSM for Protein Secondary Structure Prediction Using BP Neural Network," bmei, vol. 1, pp.107-110, 2008 International Conference on BioMedical Engineering and Informatics, 2008

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