Abstract:With people's attention to nutrition and health care functions, the prediction of grain protein function has become a research hotspot. Faced with large amounts of sequenced cereal protein genome data, the use of computational methods to predict grain protein function has become the mainstream. For the first time, the MIMLRBF algorithm was applied to protein function prediction from the grain protein domain sequence. Based on this algorithm, several improved grain protein function prediction models were proposed. Among them, an automatic adjustment coefficient for the average Hausdorff distance was introduced to calculate the similarity between proteins aiming to solve the problem that the average Hausdorff distance weakened the shortest domain distance between two proteins. At the same time, in order to improve the prediction effect, the improved MIMLRBF algorithm model was obtained using the improved hybrid radial basis kernel function to activate. Finally, the improved MIMLRBF was confirmed to be better than the traditional prediction based on the evaluation of prediction results using mainstream evaluation criteria, which proved the superiority of the proposed model.
