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SLLE for predicting membrane protein types

Authors
Journal
Journal of Theoretical Biology
0022-5193
Publisher
Elsevier
Publication Date
Volume
232
Issue
1
Identifiers
DOI: 10.1016/j.jtbi.2004.07.023
Keywords
  • Nonlinear Dimensionality Reduction
  • Slle
  • Pseudo Amino Acid Composition
  • Covariant Discriminant Algorithm
  • Chou'S Invariance Theorem
  • Membrane Protein Types
  • Bioinformatics
Disciplines
  • Biology
  • Computer Science

Abstract

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.

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