ArticleBioMed research international2013
Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm.PloS one · 2025Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway.Frontiers in genetics · 2022Article
- Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.Nature communications · 2020Article
- Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.BioImpacts : BI · 2020Article
- Synergistic drug combinations prediction by integrating pharmacological data.Synthetic and systems biotechnology · 2019Article
- Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway.BioMed research international · 2019Article
- Mathematical modeling and computational prediction of cancer drug resistance.Briefings in bioinformatics · 2018Review
- Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.International journal of molecular sciences · 2018Article
- Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features.BMC bioinformatics · 2017Article
- Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Gastroenterology · 2017Review
- Systematic synergy modeling: understanding drug synergy from a systems biology perspective.BMC systems biology · 2015Article
- Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.PloS one · 2015Article
- A hadoop-based method to predict potential effective drug combination.BioMed research international · 2014Article
- Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network.BioMed research international · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.