Abstract
Trypanosma brucei (T. brucei) is an important pathogen agent of African trypanosomiasis. The flagellum is an essential and multifunctional organelle of T. brucei, thus it is very important to recognize the flagellar proteins from T. brucei proteins for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference of probability functions of flagellar protein and the non-flagellar protein for the purpose of flagellar protein recognition. We propose to learn a multi-kernel classification function to approximate this optimal decision function, by minimizing the information loss of such approximation which is measured by the Kullback - Leibler (KL) divergence. An iterative multi-kernel classifier learning algorithm is developed to minimize the KL divergence for the problem of T. brucei flagellar protein recognition, experiments show its advantage over other T. brucei flagellar protein recognition and multi- kernel learning methods.