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Mechanistic Machine Learning for Biological Systems

7 chapters · 31 lessons

1. 1. When Black Box Models Fail in Biology

   4 lessons

   Understand why pure pattern matching breaks down in biological systems and when mechanistic approaches provide better generalization.

2. 2. Encoding Biological Constraints into Model Architecture

   5 lessons

   Learn techniques for building inductive biases and domain knowledge directly into neural network design.

3. 3. Feature Engineering from Biological Mechanisms

   4 lessons

   Extract predictive features based on molecular properties, cellular processes, and known failure modes.

4. 4. Validation Against Ground Truth Mechanisms

   5 lessons

   Design validation strategies that test whether models learn true causal relationships rather than spurious correlations.

5. 5. Building Prediction Tools for Experimental Design

   5 lessons

   Create practical systems that rank candidates, estimate uncertainty, and integrate into lab workflows.

6. 6. Hybrid Models: Combining Data and Domain Knowledge

   4 lessons

   Architect systems that balance learned patterns with explicit mechanistic rules for optimal performance.

7. 7. Communicating Model Behavior to Scientists

   4 lessons

   Develop documentation and visualization strategies that build trust and enable effective collaboration with experimental teams.