Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89292
Title: Drug-target interaction prediction via class imbalance-aware ensemble learning
Authors: Ezzat, Ali
Wu, Min
Li, Xiao-Li
Kwoh, Chee-Keong
Keywords: Drug-target Interaction Prediction
Class Imbalance
DRNTU::Engineering::Computer science and engineering
Issue Date: 2016
Source: Ezzat, A., Wu, M., Li, X.-L., & Kwoh, C.-K. (2016). Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinformatics, 17(Suppl 19), 509-. doi:10.1186/s12859-016-1377-y
Series/Report no.: BMC Bioinformatics
Abstract: Background: Multiple 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. Results: We 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. Conclusions: Our 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.
URI: https://hdl.handle.net/10356/89292
http://hdl.handle.net/10220/46173
DOI: http://dx.doi.org/10.1186/s12859-016-1377-y
Rights: © 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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