ArticleBioMed research international2014
A hadoop-based method to predict potential effective drug combination.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model).BioMed research international · 2023Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks.BMC bioinformatics · 2022Article
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019Article
- Drug Combinations: Mathematical Modeling and Networking Methods.Pharmaceutics · 2019Review
- Synergistic drug combinations prediction by integrating pharmacological data.Synthetic and systems biotechnology · 2019Article
- STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.Frontiers in bioengineering and biotechnology · 2019Article
- Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.NPJ digital medicine · 2019Review
- COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies.BMC bioinformatics · 2018Article
- ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures.Scientific reports · 2018Article
- Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.International journal of molecular sciences · 2018Article
- A survey of current trends in computational drug repositioning.Briefings in bioinformatics · 2016Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.
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