球形演化极限学习机在药物—靶标相互作用智能预测中的应用

Application of Spherical Evolution Extreme Learning Machine in Intelligent Prediction of Drug Target Interaction

为解决药物研发中湿法实验耗时长且高成本等问题,采用机器学习预测药物-靶标相互作用。同时,为解决机器学习在建立药物-靶标相互作用模型时,受到分类器的类不平衡和参数优化等各种问题的制约。文章提出了一个基于球形演化极限学习机的药物-靶相互作用预测方法(SEELM-DTI),该方法主要使用筛选法选择高置信负样本、利用球形演化算法对极限学习机的参数进行优化。该研究将SEELM-DTI与SELF-BLM、NetLapRLS、WNN-GIP、SPLCMF、BLM-NII在基准数据集中进行试验比较,评价指标为AUC与AUPR。实验结果表明:SEELM-DTI的性能和效果优于其他基准算法,并且解决了类不平衡和参数优化问题,最后在常用的多个药物数据库上验证了SEELM-DTI预测药物-靶标相互作用的效果。

To solve the problems of time-consuming and costly wet experiments for drug development,machine learning has been applied to the prediction of drug-target interactions.At the same time,in order to solve the constraints of machine learning in building drug-target interaction models,it is subject to various problems such as class imbalance of classifiers and parameter optimization.The paper proposes a drug-target interaction prediction method(SEELM-DTI)based on a spherical evolution extreme learning machine,which mainly uses a screening method to select high confidence negative samples and a spherical evolution to optimize the parameters of the extreme learning machine.The researcn compared SEELM-DTI with SELF-BLM,NetLapRLS,WNN-GIP,SPLCMF,BLM-NII in a benchmark dataset and evaluated the metrics of AUC and AUPR.The experimental results showed that the SEELM-DTI outperformed other benchmark algorithms and solved the class imbalance and parameter optimization.Finally,the effectiveness of SEELM-DTI in predicting drug-target interactions was validated on several commonly used drug databases.