Predicting protein localization in budding Yeast
Bioinformatics
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM
Pattern Recognition Letters
Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence
Computational Biology and Chemistry
Expert Systems with Applications: An International Journal
The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4
Computers in Biology and Medicine
Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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 databank. 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.