A novel predicting algorithm of thermostable proteins based on Choquet integral with respect to L-measure and Hurst exponent

Establishing a good algorithm for predicting temperature of thermostable proteins is an important issue. In this study, a novel thermostable proteins prediction method using Hurst exponent and Choquet integral regression model based on L-measure and lambda-support is proposed. The main idea of this method is to integrate the physicochemical properties, fractal property and Choquet integral regression model for amino symbolic sequences with different lengths. For evaluating the performance of this new algorithm, a 5-fold Cross-Validation MSE is performed. Experimental result shows that this new prediction scheme is better than the Choquet integral regression model based on lambda-measure and P-measure, respectively and two methods based on Hurst exponent and the traditional prediction models, ridge regression and multiple regression model, respectively.