Large-scale prediction of long disordered regions in proteins using random forests |
| |
Authors: | Pengfei Han Xiuzhen Zhang Raymond S Norton and Zhi-Ping Feng |
| |
Institution: | (1) School of Computer Science and IT, RMIT University, Melbourne, Victoria, 3001, Australia;(2) The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia |
| |
Abstract: | Background Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but
have important biological functions. Prediction of disordered regions in protein sequences is important for understanding
protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural
networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions
are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions
will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications.
Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|