title: Pattern recognition methods for protein functional site prediction.
abstract: Protein functional site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the functional sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein functional site prediction and outline the future research directions in this important area.
authors.affiliation: Department of Computer Science, University of Exeter, UK. z.r.yang@ex.ac.uk
authors.forename: Zheng Rong
authors.initials: ZR
authors.lastname: Yang
authors.forename: Lipo
authors.initials: L
authors.lastname: Wang
authors.forename: Natasha
authors.initials: N
authors.lastname: Young
authors.forename: Dave
authors.initials: D
authors.lastname: Trudgian
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D001665:Binding Sites / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins