### Sequential protein-GDP binding residues prediction

SHI Dahong, HE Xue

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
• Online:2016-07-01 Published:2016-07-15

### 序列蛋白质-GDP绑定位点预测

1. 南京理工大学 计算机科学与工程学院，南京 210094

Abstract: Accurately identifying the protein-GDP binding sites is of significant importance for both protein function analysis and drug design. Protein-GDP binding residues prediction is a typical imbalanced learning problem. Directly applying the traditional machine learning approach for this task is not suitable as the learning results will be severely biased towards the majority class. To circumvent this problem, on the basis of position specific scoring matrix feature based on sparse representation, weighted under-sampling is developed to make samples balanced. Finally support vector machine is used for prediction. Experimental results show that the proposed method achieves higher prediction performances.