基于一致性学习预测药物 - 靶标相互作用

提出了一种基于局部全局一致性（LLGC）学习的药物 - 靶标相互作用预测模型。该模型基于邻近结点及流形结构或聚类中的结点更有可能有相同标签这一结论，综合考虑靶标和药物数据的全局和局部特征，融合靶标的序列相似性和药物 - 靶标网络的拓扑结构信息，提出药物 - 靶标相互作用预测方法，挖掘来自标准数据集中的药物 - 靶标相互作用数据。为了分析局部全局一致性方法的性能，在酶、离子通道、GPCR 与核受体 4 个数据集中对此方法与 SBGI、KBMF2K、NetCBP 和 WNN-GIP 进行了比较，实验结果表明，除了在核受体数据中 LLGC 的 AUC 值比 NetCBP 和 WNN-GIP 中的略低外，在其他 3 个数据中，LLGC的性能都优于其他方法。确定模型性能后，将其用于药物 - 靶标相互作用数据预测，给出了得分最高的 5 个药物 - 靶标相互作用数据，且得知标准数据集中已知的药物 - 靶标相互作用数据绝大部分出现在预测集的前20% 中，91% 以上出现在预测集的前 50% 中。这个结果表明，LLGC 能有效预测药物与靶标之间的潜在关联。

Prediction of Drug-Target Interactions Based on Consistency Learning

A prediction model has been proposed of drug target interaction based on local and global consistency(LLGC) learning. Based on the conclusion that adjacent nodes and nodes in manifold structures or clusters are morelikely to have the same label, a comprehensive consideration is given to the global and local characteristics of targetand drug data, with the sequence similarities of targets and topological structure information of drug-target networkintegrated together, and a consistency learning method is proposed to mine the drug target interaction data from standarddata set according to standard data set. In order to analyze the performance of LLGC, a comparison has thus been madebetween the proposed method and SBGI, KBMF2K, NetCBP and WNN-GIP in four datasets of enzyme, ion channel,GPCR and nuclear receptor. The experimental results show that the performance of LLGC is better than other methodsexcept that the AUC value of LLGC in nuclear receptor data is slightly lower than that of NetCBP and WNN-GIP. Afterdetermining the performance of the LLGC model, the model is applied for the drug-target interaction prediction, withthe five drug-target interaction data with the highest score thus given. The results show that most of the known drug-target interactions in the four data sets appear in the first 20% of the prediction results, with more than 91% appearingin the first 50% of the prediction set. This result show that LLGC can effectively predict potential associations betweendrugs and targets.