ReviewBioMed research international2016
Biomolecular Network-Based Synergistic Drug Combination Discovery.
Review in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 39 citations in OpenAlex.
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- A network-driven computational framework for identifying FDA-approved drug repurposing across heterogeneous brain cancers.Frontiers in molecular biosciences · 2026Article
- A network-based trans-omics approach for predicting synergistic drug combinations.Communications medicine · 2024Article
- Novel hydrazone compounds with broad-spectrum antiplasmodial activity and synergistic interactions with antimalarial drugs.Antimicrobial agents and chemotherapy · 2024Article
- Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.Acta parasitologica · 2024Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model).BioMed research international · 2023Article
- Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks.BMC bioinformatics · 2022Article
- Advances in the computational landscape for repurposed drugs against COVID-19.Drug discovery today · 2021Review
- Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.Briefings in bioinformatics · 2021Review
- Article
- Anticancer drug synergy prediction in understudied tissues using transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.BioMed research international · 2021Article
- Folic acid-sulfonamide conjugates as antibacterial agents: design, synthesis and molecular docking studies.RSC advances · 2020Article
- Seeing the Unseen of the Combination of Two Natural Resins, Frankincense and Myrrh: Changes in Chemical Constituents and Pharmacological Activities.Molecules (Basel, Switzerland) · 2019Review
- Drug Combinations: Mathematical Modeling and Networking Methods.Pharmaceutics · 2019Review
- Prediction of drug synergy score using ensemble based differential evolution.IET systems biology · 2019Article
- Article
- 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
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
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.