计算机工程与应用2016，Vol.52Issue(13)：55-59,75,6.DOI:10.3778/j.issn.1002-8331.1407-0553
序列蛋白质-GDP绑定位点预测
Sequential protein-GDP binding residues prediction.
摘要
Abstract
Accurately identifying the protein-GDP binding sites is of significant importance for both protein function anal-ysis 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.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Key words
protein-GDP binding prediction/position specific scoring matrix/sparse representation/weighted under-sampling/support vector machine分类
信息技术与安全科学引用本文复制引用
石大宏,何雪..序列蛋白质-GDP绑定位点预测[J].计算机工程与应用,2016,52(13):55-59,75,6.基金项目
国家自然科学基金(No.61373062). （No.61373062）
