title: Support vector machines for predicting protein structural class.
abstract: BACKGROUND
  We apply a new machine learning method, the so-called Support Vector Machine method, to predict the protein structural class. Support Vector Machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure.
  
  
  RESULTS
  High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition.
  
  
  CONCLUSIONS
  It is expected that the Support Vector Machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai, 200233, China. y.cai@umist.ac.uk
authors.forename: Y D
authors.initials: YD
authors.lastname: Cai
authors.forename: X J
authors.initials: XJ
authors.lastname: Liu
authors.forename: X
authors.initials: X
authors.lastname: Xu
authors.forename: G P
authors.initials: GP
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D030562:Databases, Protein / Q000145:classification; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000145:classification*
chemical_list: D011506:Proteins