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Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

1
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
2
College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha 410082, China
3
School of Computer Science, National University of Defense Technology, Changsha 410073, China
4
School of Life Sciences, Anhui University, Hefei 230601, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(2), 467; https://doi.org/10.3390/ijms19020467
Received: 8 December 2017 / Revised: 22 January 2018 / Accepted: 30 January 2018 / Published: 5 February 2018
(This article belongs to the Section Molecular Biophysics)
Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor. View Full-Text
Keywords: drug combination; classification and prediction; improved naïve Bayesian algorithm; metabolic enzyme drug combination; classification and prediction; improved naïve Bayesian algorithm; metabolic enzyme
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MDPI and ACS Style

Bai, L.-Y.; Dai, H.; Xu, Q.; Junaid, M.; Peng, S.-L.; Zhu, X.; Xiong, Y.; Wei, D.-Q. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm. Int. J. Mol. Sci. 2018, 19, 467.

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