A novel predicting algorithm for thermostable proteins based on hurst exponent and maximized L-measure

Authors:
Hsiang-Chuan Liu;Hsien-Chang Tsai;Shin-Wu Liu
Affiliations:
Department of Bioinformatics, Asia University, Taichung County, Taiwan, ROC;Department of Biology, National Changhua University of Education, Changhua, Taiwan, ROC;Laboratory of Viral Diseases, National Institutes of Health, Maryland
Venue:
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
Year:
2010

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Abstract

Establishing a good algorithm for predicting temperature of thermostable proteins is an important issue. In this study, a new thermostable proteins prediction method by using Hurst exponent and Choquet integral regression model with respect to maximized L-measure is proposed. The main idea of this method is to integrate the physicochemical properties, long term memory property and Choquet integral regression model with respect to maximized L-measure for amino symbolic sequences of different lengths. For evaluating the performance of this new algorithm, a 5-fold Cross-Validation MSE is conducted. Experimental result shows that this new prediction algorithm is better than the Choquet integral regression model with respect to other well known fuzzy measure, Lambda-measure, P-measure, and L-measure, respectively and the traditional prediction models, ridge regression and multiple linear regression models, respectively.