ArticleNature communications2015
Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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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Who cites it
66 citing papers in PubMed, 137 citations in OpenAlex.
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- A scalable multimodal graph neural network for drug combination response prediction.Molecular diversity · 2026Article
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- AI-driven discovery of synergistic drug combinations against pancreatic cancer.Nature communications · 2025Article
- New uses of halofuginone to treat cancer.Journal of pharmaceutical analysis · 2025Review
- SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations.GigaScience · 2025Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- Aphthous stomatitis - computational biology suggests external biotic stimulus and immunogenic cell death involved.BMC oral health · 2024Article
- A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer.Interdisciplinary sciences, computational life sciences · 2024Article
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction.Quantitative biology (Beijing, China) · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Naringenin in Si-Ni-San formula inhibits chronic psychological stress-induced breast cancer growth and metastasis by modulating estrogen metabolism through FXR/EST pathway.Journal of advanced research · 2023Article
- SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.Scientific reports · 2023Article
- MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.Bioinformatics (Oxford, England) · 2023Article
- Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer.Cellular and molecular life sciences : CMLS · 2023Article
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- SimVec: predicting polypharmacy side effects for new drugs.Journal of cheminformatics · 2022Article
6 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
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.
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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.