Support vector-based Takagi-Sugeno fuzzy system for the prediction of binding affinity of peptides

High dimensional, complex and non-linear nature of the post-genome data often adversely affects the performance of predictive models. There are two methods that have been widely used to model such non-linear systems, namely Fuzzy System (FS) and Support Vector Machine (SVM). FS is good at modelling uncertainty and yielding a set of interpretable IF-THEN rules, but suffers from the curse of dimensionality whereas SVM is a method that has been shown to effectively deal with large number of dimensions leading to better generalization ability. In this paper, a hybrid system is therefore proposed to improve FS with the aid of SVM-based regression method and successfully applied to the prediction of binding affinity of peptides, which is regarded as one of the most complex modelling problems in the post-genome era due to the diversity of peptides discovered. The proposed hybrid method yields comparatively better results than what has been presented in the recently published papers, therefore can also be considered for other bioinformatics applications.