关键词: 蛋白质-GDP绑定预测, 位置特异性得分矩阵, 稀疏表示, 加权下采样, 支持向量机

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.

Key words: protein-GDP binding prediction, position specific scoring matrix, sparse representation, weighted under-sampling, support vector machine