ArticleJournal of cheminformatics2015
Systems biology approaches for advancing the discovery of effective drug combinations.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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.
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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
68 citing papers in PubMed.
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- Genistein and Butein as Bioactive Polyphenols: Molecular Targets, Metabolic Regulation, and Mechanistic Insights.Life (Basel, Switzerland) · 2026Review
- Combination Lorcaserin and Betahistine Treatment Improves Cognitive Dysfunction and Dopaminergic Neuron Activity in a Rat Model of Diet-Induced Obesity.Brain sciences · 2025Article
- HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- Multifunctional Nanoparticles as Radiosensitizers to Overcome Hypoxia-Associated Resistance in Cancer Radiotherapy.Nanomaterials (Basel, Switzerland) · 2024Article
- Article
- Fast polypharmacy side effect prediction using tensor factorization.Bioinformatics (Oxford, England) · 2024Article
- Reduction of myeloid-derived suppressor cells in prostate cancer murine models and patients following white button mushroom treatment.Clinical and translational medicine · 2024Article
- A weighted Bayesian integration method for predicting drug combination using heterogeneous data.Journal of translational medicine · 2024Article
- MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction.Molecules (Basel, Switzerland) · 2024Article
- A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models.Journal of biomechanical engineering · 2024Article
- Article
- Tumor Treating Fields (TTFields) induce homologous recombination deficiency in ovarian cancer cells, thus mitigating drug resistance.Frontiers in oncology · 2024Article
- Personalized tumor combination therapy optimization using the single-cell transcriptome.Genome medicine · 2023Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
- Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model.Journal of inflammation (London, England) · 2023Article
- Comparing the Effects of Encapsulated and Non-Encapsulated Propolis Extracts on Model Lipid Membranes and Lactic Bacteria, with Emphasis on the Synergistic Effects of Its Various Compounds.Molecules (Basel, Switzerland) · 2023Article
- Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics.Communications biology · 2022Article
- Radiosensitizing effect of galunisertib, a TGF-ß receptor I inhibitor, on head and neck squamous cell carcinoma in vitro.Investigational new drugs · 2022Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations. Graphical abstractSpectrum of Systems Biology Approaches for Drug Combinations.
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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.