ArticleNature communications2022
Bipartite network models to design combination therapies in acute myeloid leukaemia.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure.Computational and structural biotechnology journal · 2026Article
- Synergistic antiarrhythmic mechanism of IJournal of translational medicine · 2025Article
- BRD4 inhibition sensitizes glioblastoma to radiotherapy by suppressing super-enhancer-driven COL1A1.Oncogene · 2025Article
- A Network-Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Acquired resistance in cancer: towards targeted therapeutic strategies.Nature reviews. Cancer · 2025Review
- A fast approach for structural and evolutionary analysis based on energetic profile protein comparison.Nature communications · 2025Article
- Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.Oncogenesis · 2024Article
- Personalized tumor combination therapy optimization using the single-cell transcriptome.Genome medicine · 2023Article
- Potent Personalized Venetoclax Partners for Acute Myeloid Leukemia Identified by Ex Vivo Drug Screening.Blood cancer discovery · 2023Article
- Computational Advancements in Cancer Combination Therapy Prediction.JCO precision oncology · 2023Article
- Harmonizing across datasets to improve the transferability of drug combination prediction.Communications biology · 2023Article
- Prediction model for drug response of acute myeloid leukemia patients.NPJ precision oncology · 2023Article
- A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.PLoS computational biology · 2023Article
- Discovery of druggable cancer-specific pathways with application in acute myeloid leukemia.GigaScience · 2022Article
- PINet 1.0: A pathway network-based evaluation of drug combinations for the management of specific diseases.Frontiers in molecular biosciences · 2022Article
- Link Prediction in Multipartite Graphs With Application to Drug Repositioning Studies.IEEE transactions on computational biology and bioinformaticsArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.
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