Machine learning for biology
Alphafold, connectomics, and other applications
January 1, 2025 — January 1, 2025
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.