Machine learning for biology
Alphafold, connectomics, and other applications
2025-01-01 — 2025-01-01
calculus
dynamical systems
geometry
Hilbert space
how do science
machine learning
neural nets
PDEs
physics
regression
sciml
SDEs
signal processing
statistics
statmech
stochastic processes
surrogate
time series
uncertainty
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Levers for Biological Progress - by Niko McCarty
In order for 50-100 years of biological progress to be condensed into 5-10 years of work, we’ll need to get much better at running experiments quickly and also collecting higher-quality datasets. This essay focuses on how we might do both, specifically for the cell. Though my focus in this essay is narrow — I don’t discuss bottlenecks in clinical trials, human disease, or animal testing — I hope others will take on these challenges in similar essays.
1 References
Jamieson, and Jain. n.d. “A Bandit Approach to Multiple Testing with False Discovery Control.”
Scannell, Blanckley, Boldon, et al. 2012. “Diagnosing the Decline in Pharmaceutical R&D Efficiency.” Nature Reviews Drug Discovery.
Wang, Fu, Du, et al. 2023. “Scientific Discovery in the Age of Artificial Intelligence.” Nature.