Taha Y. Taha
@taha801.bsky.social
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Taha Y. Taha
Sarah Gurev
4 months ago
🦠The future of pathogen forecasting needs rigorous benchmarks and domain-specific modeling, not only bigger PLMs. EVEREST is a step in that direction. 🔗Paper:
biorxiv.org/content/10.1...
💻Code + data:
github.com/debbiemarksl...
12/12
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Variant effect prediction with reliability estimation across priority viruses
Viruses pose a significant threat to global health due to their rapid evolution, adaptability, and increasing potential for cross-species transmission. While advances in machine learning and the growi...
https://biorxiv.org/content/10.1101/2025.08.04.668549v1
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I finally know why the cap was designed that way.....
7 months ago
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reposted by
Taha Y. Taha
Jorge Bravo Abad
10 months ago
Kraemer et al. use machine learning to model epidemics more efficiently. Their work shows faster forecasting, better handling of incomplete data, and more accurate infection estimates, pointing to improved global health decision making.
www.nature.com/articles/s41...
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Artificial intelligence for modelling infectious disease epidemics - Nature
This Perspective considers the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science.
https://www.nature.com/articles/s41586-024-08564-w
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Taha Y. Taha
Stephan Hacker
11 months ago
Interesting bioRxiv preprint by a team around Charles Craik, Adam Renslo and @theottlab.bsky.social. Inhibitors for Mpro of coronaviruses based on a dihydrouracil chemotype and a propargyl amide warhead as reactive group to engage the catalytic cysteine. #chemsky #ChemBio
www.biorxiv.org/cont...
#chemsky
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Thrilled to share our latest work identifying that
#SARSCoV2
#Omicron
variants harboring an L260F mutation in NSP6 have enhanced replication and pathogenesis. A not so brief thread 🧵
11 months ago
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