Optimized Ensemble Machine Learning Framework for High Dimensional Imbalanced Bio Assays

Received: 22 July 2019 Accepted: 30 September 2019 In pharmaceutical research, a recent hotspot is the study of the activity of bioactive compounds and drugs with computational intelligence. The relevant studies often adopt machine learning techniques to speed up the modelling, and rely on bioassay to evaluate the effect and potency of a compound or drug. This paper aims to design an efficient and accurate method to assess the activity of bioactive compounds and drugs. First, the authors performed virtual screening on the data on bioactive compounds and drugs, eliminating the imbalanced classes and high dimensionality of drug descriptors. Next, eight machine learning algorithms, namely Bayes Net, Naive Bayes, SMO, J48, Random Forest, AdaBoost, AdaBag and logistic regression, were trained by the virtually screened data, and used to predict the activity or inactivity of a drug through bioassays. The synthetic minority oversampling technique (SMOTE) was employed to solve the numerous imbalanced datasets in bioassay. On this basis, the ensemble machine learning model of random forest was optimized. Experimental results show that the optimized random forest machine learning framework achieved better results than the other ensemblebased machine learning methods. The research provides an effective way to perform bioassays on high-dimensional imbalanced data.