ArticleFrontiers in systems biology2023
Generating synthetic multidimensional molecular time series data for machine learning: considerations.
Article in Frontiers in systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Optimizing metabolic health with digital twins.npj aging · 2025Review
- Immune digital twins for complex human pathologies: applications, limitations, and challenges.NPJ systems biology and applications · 2024Review
- Article
- The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.bioRxiv : the preprint server for biology · 2024Article
- Forum on immune digital twins: a meeting report.NPJ systems biology and applications · 2024Review
- A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.Frontiers in systems biology · 2023Article
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Abstract
The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use
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