Abstract
Drug-target interaction prediction is very important in drug development. Since determining drug-target interactions is costly and time-consuming by experiments, it is a complement to determine the interactions by computational method. To address the issue, a random projection ensemble approach is proposed and drug-compounds are encoded with feature descriptors by software “PaDEL-Descriptor”, while target proteins are encoded with physicochemical properties of amino acids. From 544 properties in AAindex1, 34 relatively independent physicochemical properties are extracted. Random projection on the vector of drug-target pair with different dimensions can map the original space onto a reduced one and thus yield a transformed vector with fixed dimension. Several random projections build an ensemble REPTree system. Experimental results showed that our method significantly outperformed and ran faster than other state-of-the-art drug-target predictors.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Nos. 61300058, 61271098 and 61472282).
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Chen, P., Hu, S., Wang, B., Zhang, J. (2015). A Random Projection Ensemble Approach to Drug-Target Interaction Prediction. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_72
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DOI: https://doi.org/10.1007/978-3-319-22053-6_72
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