https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7
Ziemann, M., Eren, Y., & El-Osta, A. (2016). Gene name errors are widespread in the scientific literature. Genome Biology, 17(1). https://doi.org/10.1186/s13059-016-1044-7
Archive for the 'SciLit' Category
Gene name errors are widespread in the scientific literature | Genome Biology | Full Text
July 26, 2025Scalable emulation of protein equilibrium ensembles with generative deep learning | Science
July 12, 2025https://www.science.org/doi/10.1126/science.adv9817
Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., Chen, Y., Yang, S., Foster, A. E., Schneuing, A., Nigam, J., Barbero, F., Stimper, V., Campbell, A., Yim, J., . . . Noé, F. (2025, July 10). Scalable emulation of protein equilibrium ensembles with generative deep learning. Science.
https://www.science.org/doi/10.1126/science.adv9817
Aβ∗56 is a stable oligomer that impairs memory function in mice – PMC
June 26, 2025https://pmc.ncbi.nlm.nih.gov/articles/PMC10905009/
Liu, P., Lapcinski, I. P., Hlynialuk, C. J., Steuer, E. L., Loude, T. J., Shapiro, S. L., Kemper, L. J., & Ashe, K. H. (2024). Aβ∗56 is a stable oligomer that impairs memory function in mice. iScience, 27(3), 109239. https://doi.org/10.1016/j.isci.2024.109239
Aβ∗56 is a ∼56-kDa, SDS-stable, A11-reactive, non-plaque-dependent, water-soluble, brain-derived oligomer containing canonical Aβ(1-40).
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development – PMC
June 10, 2025https://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
Mastering diverse control tasks through world models | Nature
April 26, 2025https://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
The Abstract: 8 Compounds That Target Aging
March 18, 2025Guarente, 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.
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Assessing the safety of new germicidal far‐UVC technologies – Görlitz – 2024 – Photochemistry and Photobiolog y – Wiley Online Library
March 16, 2025https://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
Far-UVC (222 nm) efficiently inactivates an airborne pathogen in a room-sized chamber | Scientific Reports
March 16, 20251906.02691 An Introduction to Variational Autoencoders
March 2, 2025Has 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
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