Pattern Recognition Methods for Protein Functional Site Prediction

Title: Pattern Recognition Methods for Protein Functional Site Prediction

Volume: 6 Issue: 5

Author(s): Zheng R. Yang, Lipo Wang, Natasha Young and Kuo-Chen Chou

Affiliation:

Keywords: hiv protease, distorted-key theory, sliding window, neural network, support vector machines, bio-basis function classifier

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.

Cite this article as:

Yang R. Zheng, Wang Lipo, Young Natasha and Chou Kuo-Chen, Pattern Recognition Methods for Protein Functional Site Prediction, Current Protein & Peptide Science 2005; 6 (5) . https://dx.doi.org/10.2174/138920305774329322