Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier
It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein data-bank. In this study, based on the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, a novel approximate entropy (ApEn) based PseAA composition is proposed to represent apoptosis protein sequences. An ensemble classifier is introduced, of which the basic classifier is the FKNN (fuzzy K-nearest neighbor) one, as prediction engine. Each basic classifier is trained in different dimensions of PseAA composition of protein sequences. The immune genetic algorithm (IGA) is used to search the optimal weight factors in generating the PseAA composition for crucial of weight factors in PseAA composition. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for protein function, or at least can play a complimentary role to the existing methods in the relevant areas. (C) 2008 Elsevier B.V. All rights reserved.
pseudo amino acid composition ensemble classifier approximate entropy apoptosis protein subcellular location fuzzy k-nearest neighbor classifier