Contextual AI models for context-specific prediction in biology
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41592-024-02342-2.pdf
Reference5 articles.
1. Pan, J. et al. Sparse dictionary learning recovers pleiotropy from human cell fitness screens. Cell Syst. 13, 286–303 (2022). This paper describes a framework to disentangle gene functions that vary across cell contexts defined by fitness screens.
2. Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023). This paper introduces a deep learning model using sequence and structural contexts for pathogenicity prediction of missense variants.
3. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This paper describes the AlphaFold model for 3D structure protein folding prediction, which incorporates physical and biological priors into protein modeling.
4. CZI Single-Cell Biology Program et al. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Preprint at https://doi.org/10.1101/2023.10.30.563174 (2023). This paper describes a data platform with curated and interoperable single-cell datasets across healthy and disease states.
5. Sheridan, C. Can single-cell biology realize the promise of precision medicine? Nat. Biotechnol. 42, 159–162 (2024). This news article outlines the current state of the single-cell biology field and its potential for precision medicine.
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