https://pmc.ncbi.nlm.nih.gov/articles/PMC11513550/
Ponce‐Bobadilla, A. V., Schmitt, V., Maier, C. S., Mensing, S., & Stodtmann, S. (2024). Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development. Clinical and Translational Science, 17(11).
https://doi.org/10.1111/cts.70056
https://www.nature.com/articles/s41586-025-08744-2
Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025). Mastering diverse control tasks through world models. Nature.
https://doi.org/10.1038/s41586-025-08744-2
Guarente, L., Sinclair, D. A., & Kroemer, G. (2024). Human trials exploring anti-aging medicines. Cell Metabolism, 36(2), 354–376. https://doi.org/10.1016/j.cmet.2023.12.007
https://www.cell.com/cell-metabolism/fulltext/S1550-4131(23)00458-8
QT:{{”
In a recent issue of Cell Metabolism, Guarente co-authored a review article about human trials exploring compounds that target pathways and mechanisms of aging along with David Sinclair, Ph.D., one of Dr. Guarente’s postdoctoral mentees and now a professor of genetics at Harvard Medical School, and Guido Kroemer, M.D., Ph.D., a professor at the Université Paris Cité. Guarente and his colleagues focus on eight drugs and compounds: metformin, NAD+ precursors, glucagon-like peptide-1 receptor agonists, TORC1 inhibitors, spermidine, senolytics, probiotics, and anti-inflammatories.
These interventions made the list for four reasons: 1) they’re well-represented in ongoing or completed human clinical trials; 2) they’ve been shown to slow aging in preclinical studies; 3) they’re thought to be sufficiently safe for long-term use in humans; and 4) they work by targeting the hallmarks of aging.
“}}
https://onlinelibrary.wiley.com/doi/10.1111/php.13866
Görlitz, M., Justen, L., Rochette, P. J., Buonanno, M., Welch, D., Kleiman, N. J., Eadie, E., Kaidzu, S., Bradshaw, W. J., Javorsky, E., Cridland, N., Galor, A., Guttmann, M., Meinke, M. C., Schleusener, J., Jensen, P., Söderberg, P., Yamano, N., Nishigori, C., . . . Esvelt, K. (2023). Assessing the safety of new germicidal far‐UVC technologies. Photochemistry and Photobiology, 100(3), 501–520.
https://doi.org/10.1111/php.13866
Has detailed setup for ELBO
https://arxiv.org/abs/1906.02691
Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392. https://doi.org/10.1561/2200000056
https://www.biorxiv.org/content/10.1101/2024.05.08.593025v1
Chen, J., Xiong, C., Sun, Q., Wang, G. W., Gupta, G. P., Halder, A., Li, Y., & Li, D. (2024). Investigating spatial dynamics in spatial omics data with StarTrail. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.05.08.593025
https://arxiv.org/abs/1803.00567
explains the dual problem well
Peyré, G., & Cuturi, M. (2018, March 1). Computational Optimal transport. arXiv.org. https://arxiv.org/abs/1803.00567
tutorial_on_optimal_transport.pdf
https://arxiv.org/abs/2312.07511
Duval, A., Mathis, S., V., Joshi, C. K., Schmidt, V., Miret, S., Malliaros, F. D., Cohen, T., Liò, P., Bengio, Y., & Bronstein, M. (2023, December 12). A Hitchhiker’s guide to Geometric GNNs for 3D atomic Systems. arXiv.org. https://arxiv.org/abs/2312.07511
Good intuition on spherical harmonics
linkstream2 microblog 