Generating synthetic multidimensional molecular time series data for machine learning: considerations.

Area of Science:

• Biomedical Artificial Intelligence (AI)
• Computational Biology
• Machine Learning (ML)

Background:

• Synthetic data generation is crucial for AI, but current methods are insufficient for biomedical time series data.
• Existing techniques struggle with data sparsity, the Curse of Dimensionality, and biological system complexity.
• There's a critical gap in generating synthetic multi-dimensional molecular time series data (SMTs) for AI.

Purpose of the Study:

• To address the limitations of current synthetic data generation methods for AI in biomedical research.
• To propose and justify a novel approach for generating synthetic mediator trajectories (SMTs).
• To enhance the development of AI systems for disease forecasting and drug development.

Main Methods:

• Critique of statistical and data-centric ML approaches for SMT generation.
• Advocacy for complex multi-scale mechanism-based simulation models.
• Incorporation of principles like Maximal Entropy to handle epistemic incompleteness.

Main Results:

• Proposed simulation models can generate SMTs that overcome limitations of existing methods.
• The approach accounts for perpetual data sparsity and biological system complexity.
• Generated SMTs minimize overfitting and enhance generalizability in AI models.

Conclusions:

• Complex multi-scale simulation models offer a viable solution for generating high-quality SMTs.
• This advancement is essential for developing robust AI-driven biomarker and mediator signature forecasting systems.
• Improved SMT generation supports optimized therapeutic control development and drug discovery pipelines.