ArticleNPJ systems biology and applications2019
Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.
Article in NPJ systems biology and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox.Nature communications · 2025Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets.PeerJ. Computer science · 2025Article
- A network-based trans-omics approach for predicting synergistic drug combinations.Communications medicine · 2024Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
- ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.Frontiers in oncology · 2024Article
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.NAR genomics and bioinformatics · 2022Article
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
- Construction and contextualization approaches for protein-protein interaction networks.Computational and structural biotechnology journal · 2022Review
- Artificial intelligence unifies knowledge and actions in drug repositioning.Emerging topics in life sciences · 2021Review
- Prediction and identification of synergistic compound combinations against pancreatic cancer cells.iScience · 2021Article
- A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data.BMC complementary medicine and therapies · 2021Article
- Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.Pharmacogenomics · 2021Review
- Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.BMC medical informatics and decision making · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- PLK1 and NOTCH Positively Correlate in Melanoma and Their Combined Inhibition Results in Synergistic Modulations of Key Melanoma Pathways.Molecular cancer therapeutics · 2021Article
- Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics.Frontiers in pharmacology · 2021Article
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9 authors.
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Abstract
Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for
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