ReviewComputational and structural biotechnology journal2022
Systematic review of computational methods for drug combination prediction.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed.
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.Journal of cheminformatics · 2026Article
- DualKG-DC: A Drug-Centric Dual-Layer Knowledge Graph Framework for Drug Combination Prediction.Journal of medical systems · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies.bioRxiv : the preprint server for biology · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- A Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.Cancer research · 2025Article
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- Scaling up drug combination surface prediction.Briefings in bioinformatics · 2025Article
- Advancing personalized cancer therapy: Onko_DrugCombScreen-a novel Shiny app for precision drug combination screening.NAR genomics and bioinformatics · 2025Article
- Identifying traditional Chinese medicine combinations for breast cancer treatment based on transcriptional regulation and chemical structure.Chinese medicine · 2025Article
- Predicting drug combination response surfaces.npj drug discovery · 2025Article
- Combining treatments for migraine prophylaxis: the state-of-the-art.The journal of headache and pain · 2024Review
- Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.Briefings in bioinformatics · 2024Article
- DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.Briefings in bioinformatics · 2024Article
- iDOMO: identification of drug combinations via multi-set operations for treating diseases.Briefings in bioinformatics · 2024Article
- Article
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic
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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.