https://www.science.org/doi/10.1126/science.1225829
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable Dual-RNA–Guided DNA
endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829
https://www.nature.com/articles/s41586-026-10265-5
Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., Ha, D., & Clune, J. (2026). Towards end-to-end automation of AI research. Nature, 651(8107), 914–919. https://doi.org/10.1038/s41586-026-10265-5
game theory papers
https://www.science.org/doi/10.1126/sciadv.abe9986
Wan, Z., Vorobeychik, Y., Xia, W., Liu, Y., Wooders, M., Guo, J., Yin, Z., Clayton, E. W., Kantarcioglu, M., & Malin, B. A. (2021). Using game theory to thwart multistage privacy intrusions when sharing data. Science Advances, 7(50), eabe9986.
https://doi.org/10.1126/sciadv.abe9986
Guo, J., Clayton, E. W., Kantarcioglu, M., Vorobeychik, Y., Wooders, M., Wan, Z., Yin, Z., & Malin, B. A. (2023). A game theoretic approach to balance privacy risks and familial benefits. Scientific Reports, 13(1), 6932. https://doi.org/10.1038/s41598-023-33177-0
They seem to be more focused on the cost to the attacker
Interesting paper on the Aging Brain. Featured in NY Times.
Nature https://www.nature.com/articles/s41586-026-10169-4
Disouky, A., Sanborn, M. A., Sabitha, K. R., Mostafa, M. M., Ayala, I. A., Bennett, D. A., Lu, Y., Zhou, Y., Keene, C. D., Weintraub, S., Gefen, T., Mesulam, M., Geula, C., Maienschein-Cline, M., Rehman, J., & Lazarov, O. (2026). Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease. Nature.
https://doi.org/10.1038/s41586-026-10169-4
https://www.nature.com/articles/nrdp20155
nrdp20155.pdf
Bates, G. P., Dorsey, R., Gusella, J. F., Hayden, M. R., Kay, C., Leavitt, B. R., Nance, M., Ross, C. A., Scahill, R. I., Wetzel, R., Wild, E. J., & Tabrizi, S. J. (2015). Huntington disease. Nature Reviews Disease Primers, 1(1), 15005.
https://doi.org/10.1038/nrdp.2015.5
https://pmc.ncbi.nlm.nih.gov/articles/PMC3672958/
Goldman, N., Bertone, P., Chen, S., Dessimoz, C., LeProust, E. M., Sipos, B., & Birney, E. (2013). Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature, 494(7435), 77–80. https://doi.org/10.1038/nature11875
QT:{{”
The DROP-AD project investigates the potential of dried plasma spot (DPS) and dried blood spot (DBS) analysis, derived from capillary blood, for detecting AD biomarkers, including phosphorylated tau at amino acid 217 (p-tau217), glial fibrillary acidic protein and neurofilament light. …. Similarly, we demonstrated the successful detection of glial fibrillary acidic protein and neurofilament light with strong correlations between DBS and DPS, respectively, using paired venous plasma samples.
“}}
Might find this paper very interesting. Just published this month in Nature Medicine. “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology.”
A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology – Nature Medicine
https://www.nature.com/articles/s41591-025-04080-0
https://xpresso.gs.washington.edu/
Agarwal V, Shendure J. Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. 2020. Cell Reports 31 (7), 107663.
https://github.com/calico/borzoi
N&V – https://www.nature.com/articles/s41588-025-02154-w
https://www.nature.com/articles/s41588-024-02053-6#:~:text=This%20paper%20proposes%20a%20new%20machine%2Dlearning%20model%2C,that%20drive%20RNA%20expression%20and%20post%2Dtranscriptional%20regulation QT:{{” ere, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. “}}
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025). Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics, 57(4), 949–961.
https://doi.org/10.1038/s41588-024-02053-6
https://www.nature.com/articles/s41467-024-54441-5
QT:{{” Enformer Celltyping can predict in new cell types, imputing their epigenetic signal, by embedding global and local chromatin accessibility (ATAC-Seq) signals for the cell type of interest. “}}
Murphy, A. E., Beardall, W., Rei, M., Phuycharoen, M., & Skene, N. G. (2024). Predicting cell type-specific epigenomic profiles accounting for distal genetic effects. Nature Communications, 15(1), 9951. https://doi.org/10.1038/s41467-024-54441-5
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