newsNature Machine IntelligenceTrust 88 · LabPublished 2mo agoLive · 1mo ago
From virtual experiments to biomedical insight with synthetic data
Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01244-6 Synthetic datasets are becoming crucial for the development of biomedical machine learning models. Victoriano et al. discuss the persistent simulation-to-reality gap that limits how well synthetic performance predicts real-world performance.
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- LinkedLinked via unknownBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark →
- LinkedLinked via unknownHuman-Machine Collaboration on Generative Meta-Learning: Model and Algorithm →
- PossiblePossibly related (embedding) · 52%rasinmuhammed/misata →
- PossiblePossibly related (embedding) · 46%Synthetic-to-Real Translation for Class-Agnostic Motion Prediction →
- PossiblePossibly related (embedding) · 50%RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models →
- PossiblePossibly related (embedding) · 49%nolabs-ai/deepfabric →
- PossiblePossibly related (embedding) · 58%SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking →
- PossiblePossibly related (embedding) · 46%Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics →
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paperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperHuman-Machine Collaboration on Generative Meta-Learning: Model and Algorithmreporasinmuhammed/misatapaperSynthetic-to-Real Translation for Class-Agnostic Motion PredictionpaperRMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Modelsreponolabs-ai/deepfabricpaperSYNRARE: Synthetic Rare Disease EHR Generation for ML BenchmarkingpaperEntropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical KineticspaperMetric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AIreponct_tso_public/nonrigid-data-generation-pipeline
Related across the graph
paperEntropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kineticsreponct_tso_public/nonrigid-data-generation-pipelinepaperHuman-Machine Collaboration on Generative Meta-Learning: Model and AlgorithmpaperSYNRARE: Synthetic Rare Disease EHR Generation for ML BenchmarkingpaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperSynthetic-to-Real Translation for Class-Agnostic Motion PredictionpaperMetric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AIpaperRMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Modelsreponolabs-ai/deepfabricreporasinmuhammed/misata