ArticleBioinformatics (Oxford, England)2014
Combinatorial therapy discovery using mixed integer linear programming.
Article in Bioinformatics (Oxford, England), 2014. 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.
- 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
- HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.BMC bioinformatics · 2025Article
- SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing.Bioinformatics advances · 2025Article
- Understanding the ancient classic and famous prescriptionsFrontiers in pharmacology · 2025Article
- A novel combination treatment for fragile X syndrome predicted using computational methods.Brain communications · 2024Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
- MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders.PLoS computational biology · 2023Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective.Nature communications · 2022Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- From cell lines to cancer patients: personalized drug synergy prediction.Bioinformatics (Oxford, England) · 2022Article
- Prediction of synergistic drug combinations using PCA-initialized deep learning.BioData mining · 2021Article
- Network-principled deep generative models for designing drug combinations as graph sets.Bioinformatics (Oxford, England) · 2020Article
- Coexpression enrichment analysis at the single-cell level reveals convergent defects in neural progenitor cells and their cell-type transitions in neurodevelopmental disorders.Genome research · 2020Article
- DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy.Nucleic acids research · 2020Article
- Deep graph embedding for prioritizing synergistic anticancer drug combinations.Computational and structural biotechnology journal · 2020Article
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.BMC bioinformatics · 2019Article
- High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration.Cell systems · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
motivationCombinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction.
resultsHere we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. AVAILABILITY: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. CONTACT: zhandong.liu@bcm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.