ArticleNature biomedical engineering2023
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.
Article in Nature biomedical engineering, 2023. 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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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
27 citing papers in PubMed.
- Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.NPJ digital medicine · 2026Article
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Development of a novel organoid-based two-dimensional system to model the intestinal host-parasite interface in fasciolosis.Frontiers in cellular and infection microbiology · 2026Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Early host-parasite interaction models reveal a key role for fibrinolysis in Fasciola hepatica intestinal migration.Parasites & vectors · 2025Article
- Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.Cell genomics · 2025Article
- EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.Briefings in bioinformatics · 2025Article
- Advances and challenges in drug design against dental caries: Application ofJournal of pharmaceutical analysis · 2025Review
- Building a unified model for drug synergy analysis powered by large language models.Nature communications · 2025Article
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.Health care science · 2024Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- Learning chemical sensitivity reveals mechanisms of cellular response.Communications biology · 2024Article
- Advancing cancer nanomedicine with machine learning.Acta pharmaceutica Sinica. B · 2024Article
- An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.Nature communications · 2024Article
- Harnessing the deep learning power of foundation models in single-cell omics.Nature reviews. Molecular cell biology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.
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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.