ArticleInternational journal of molecular sciences2018
Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances.BMC medical informatics and decision making · 2023Article
- Identification of a Family of Glycoside Derivatives Biologically Active againstPharmaceuticals (Basel, Switzerland) · 2023Article
- CDCDB: A large and continuously updated drug combination database.Scientific data · 2022Article
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
- Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine.Scientific reports · 2019Article
- Drug Combinations: Mathematical Modeling and Networking Methods.Pharmaceutics · 2019Review
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
- Synergistic drug combinations prediction by integrating pharmacological data.Synthetic and systems biotechnology · 2019Article
- Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.BioMed research international · 2019Article
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Authors and funding
8 authors.
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Abstract
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.
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