A Novel Approach Predicting the Signal Peptides and Their Cleavage Sites

The sliding window method will cause the severe unbalanced dataset problem. In this paper, under-sample the majority class method is adopted to solve this problem, and SVM is used to classify the processed data. The better prediction result of minority class (that is, the signal peptides positive sample set) is observed. Besides, we discover that the (-3,-1) rule is helpful to the prediction. So Information content based feature weighting method is proposed. This method avoids the blindness of the previous algorithm in dealing with different sites. Experiments show that not only is the correct prediction rate of minority class improved dramatically, but also the correct prediction rate of majority class is kept in a high level. Combination of the unbalanced data processing and the proposed information content based feature weighting method can greatly improve the performance of SVM classifier of signal peptides.