Drug-target interaction prediction via class imbalance-aware ensemble learning

Abstract

BackgroundMultiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types.ResultsWe propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully.ConclusionsOur proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1377-y) contains supplementary material, which is available to authorized users.

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

“…These results demonstrate the effectiveness of using our newly proposed features for prediction of drug targets. Furthermore, our classifier outperforms other drug-target prediction methods published in recent years [12,19,31,32] (Fig. 4d), achieving favorable performance with a highly imbalanced dataset.…”

“…Nevertheless, two fallacies are commonly overlooked: conventional train-test splitting and crossvalidation schemes are flawed for pair-input prediction tasks [18]; extreme class imbalance in drug target datasets is not satisfactorily addressed by commonly used methods such as sampling from the majority class [19]. Moreover, most methods lack the ability to predict drug-target interactions for genes that are not yet known to be druggable.…”

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

“…As a consequence, researchers have little choice other than treating all unverified DTIs as negative samples despite that some of them may be true DTIs. Several recent applications were proposed to tackle this problem by treating noninteraction pairs as unlabeled (88,89), building up highly credible negative samples (44), or class imbalance-aware ensemble learning (90). Nevertheless, these techniques may be overly simplified according to a recent review (17).…”

Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016)

“…As the number of known drug-target interactions far exceeds the potential interactions of these drugs with other potential targets, predicting the repurposing of a drug appears to be extremely challenging. Ezzat et al [21] have addressed this class imbalance problem using an ensemble learning approach to successfully predict several new drug-target interactions. Sun et al [22] have addressed the same challenge using a Physarum-inspired Prize-Collecting Steiner Tree algorithm, to build drug similarity networks, from which ten frequently occurring drug molecules have been reported as potential new cardiovascular therapeutic agents.…”