ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid composition
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Structural and Functional Bioinformatics Group
Permanent link to this recordhttp://hdl.handle.net/10754/562173
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AbstractKnowing the type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane protein types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible types of membrane proteins. In view of this, various computational membrane protein prediction methods have been proposed. They extract protein feature vectors, such as PseAAC (pseudo amino acid composition) and PsePSSM (pseudo position-specific scoring matrix) for representation of protein sequence, and then learn a distance metric for the KNN (K nearest neighbor) or NN (nearest neighbor) classifier to predicate the final type. Most of the metrics are learned using linear dimensionality reduction algorithms like Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Such metrics are common to all the proteins in the dataset. In fact, they assume that the proteins lie on a uniform distribution, which can be captured by the linear dimensionality reduction algorithm. We doubt this assumption, and learn local metrics which are optimized for local subset of the whole proteins. The learning procedure is iterated with the protein clustering. Then a novel ensemble distance metric is given by combining the local metrics through Tikhonov regularization. The experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of the proposed algorithm named ProClusEnsem. © 2012 Elsevier Ltd.
SponsorsThe study was supported by grants from Shanghai Key Laboratory of Intelligent Information Processing, China (Grant No. IIPL-2011-003), Key Laboratory of High Performance Computing and Stochastic Information Processing, Ministry of Education of China (Grant No. HS201107), National Grand Fundamental Research (973) Program of China (Grant Nos. 2010CB834303 and 2011CB911102), National Natural Science Foundation of China (Grant No. 60973154), Hubei Provincial Science Foundation, China (Grant Nos. 2010CDA006 and 2010CD06601), and a grant from King Abdullah University of Science and Technology.
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