Prediction of protein structural class using a complexity-based distance measure |
| |
Authors: | Taigang Liu Xiaoqi Zheng Jun Wang |
| |
Institution: | (1) Department of Applied Mathematics, Dalian University of Technology, 116024 Dalian, China;(2) College of Advanced Science and Technology, Dalian University of Technology, 116024 Dalian, China;(3) Scientific Computing Key Laboratory of Shanghai Universities, Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China |
| |
Abstract: | Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop
effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method
called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from
protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity
measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance
of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach
achieves a high prediction accuracy over some classical methods. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|