title: TOPPER: topology prediction of transmembrane protein based on evidential reasoning.
abstract: The topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on various principles such as the hydrophobicity or charges of residues. In this paper, an evidential topology prediction method for transmembrane protein is proposed based on evidential reasoning, which is called TOPPER (topology prediction of transmembrane protein based on evidential reasoning). In the proposed method, the prediction results of multiple individual prediction algorithms can be transformed into BPAs (basic probability assignments) according to the confusion matrix. Then, the final prediction result can be obtained by the combination of each individual prediction base on Dempster's rule of combination. The experimental results show that the proposed method is superior to the individual prediction algorithms, which illustrates the effectiveness of the proposed method.
authors: School of Computer and Information Science, Southwest University, Chongqing 400715, China.
authors: Xinyang
authors: X
authors: Deng
authors: Qi
authors: Q
authors: Liu
authors: Yong
authors: Y
authors: Hu
authors: Yong
authors: Y
authors: Deng
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results
chemical_list: D008565:Membrane Proteins
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