Feature Selection and Combination Criteria for Improving Predictive Accuracy in Protein Structure Classification

Authors:
Chun Yuan Lin;Ken-Li Lin;Chuen-Der Huang;Hsiu-Ming Chang;Chiao Yun Yang;Chin-Teng Lin;Chuan Yi Tang;D. Frank Hsu
Affiliations:
National Tsing Hua University;National Chiao Tung University and Chung Hua University;Hsiuping Institute of Technology;National Tsing Hua University;National Tsing Hua University;National Chiao Tung University;National Tsing Hua University;Fordham University
Venue:
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Year:
2005

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Improving prediction accuracy for protein structure classification by neural network using feature combination

AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications

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Abstract

The classification of protein structures is essential for their function determination in bioinformatics. The success of the protein structure classification depends on two factors: the computational methods used and the features selected. In this paper, we use a combinatorial fusion analysis technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying these criteria to our previous work, the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than our previous work and demonstrate that combinatorial fusion is a valuable method for protein structure classification.