title: SLLE for predicting membrane protein types.
abstract: Introduction of the concept of pseudo amino acid composition (PROTEINS: Structure, Function, and Genetics 43 (2001) 246; Erratum: ibid. 44 (2001) 60) has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, and hence can significantly enhance the prediction quality of membrane protein type. As a continuous effort along such a line, the Supervised Locally Linear Embedding (SLLE) technique for nonlinear dimensionality reduction is introduced (Science 22 (2000) 2323). The advantage of using SLLE is that it can reduce the operational space by extracting the essential features from the high-dimensional pseudo amino acid composition space, and that the cluster-tolerant capacity can be increased accordingly. As a consequence by combining these two approaches, high success rates have been observed during the tests of self-consistency, jackknife and independent data set, respectively, by using the simplest nearest neighbour classifier. The current approach represents a new strategy to deal with the problems of protein attribute prediction, and hence may become a useful vehicle in the area of bioinformatics and proteomics.
authors.affiliation: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 200030, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Zhi-Jie
authors.initials: ZJ
authors.lastname: Xu
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis*; D000818:Animals; D019295:Computational Biology / Q000379:methods; D008565:Membrane Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins