Research on Prediction and Optimization of Cereal Protein Secondary Structure Based on Multi-Kernel LSSVM

Abstract:

The secondary structure of protein has an extremely important impact on their spatial structure and function. Using machine learning methods to predict the secondary structure of grain proteins is one of the important topics in the fields of biology, food, etc.In this experiment, three grain proteins of corn, wheat, and soybeans were selected from the existing protein data bank,using multi-feature fusion method to extract features of protein sequence, and propose to combine multi-core learning with least square support vector machine classifier, the linear weighted combination of multiple kernel functions is used to replace the traditional single kernel function, and the kernel weight is used to adjust the fusion effect to construct a multi-core LSSVM prediction model to predict the secondary structure of grain proteins.The particle swarm optimization algorithm is used to optimize the model hyper parameters and find the best hyper parameter combination to improve the prediction performance of the model. The experimental results prove that the multi-kernel LSSVM algorithm proposed in this paper can make up for the limitations of single kernel function high-dimensional mapping, integrate the advantages of each kernel function, and find the best hyperparameter combination through the PSO algorithm, combine multiple feature extraction methods to significantly improve the Q₃ accuracy of grain protein secondary structure prediction.