Francesco Paolo Casale | ML-enabled Genetic Association Studies of High-Content Pheno... | CGSI 2024

Francesco Paolo Casale | ML-enabled Genetic Association Studies of High-Content Phenotypes | CGSI 2024
Related Papers:
[1] Chaudhary, S., Voigts, A., Bereket, M., Albert, M. L., Schwamborn, K., Zeggini, E., & Casale, F. P. (2024). HistoGWAS: An AI-enabled Framework for Automated Genetic Analysis of Tissue Phenotypes in Histology Cohorts. bioRxiv.
[2] Gräf, L., Sens, D., Shilova, L., & Casale, F. P. (2024). Disease Risk Predictions with Differentiable Mendelian Randomization. In International Conference on Research in Computational Molecular Biology (pp. 385-389). Cham: Springer Nature Switzerland.
[3] Engelmann, J. P., Palma, A., Tomczak, J. M., Theis, F., & Casale, F. P. (2024). Mixed Models with Multiple Instance Learning. In International Conference on Artificial Intelligence and Statistics (pp. 3664-3672). PMLR.
[4] Casale, F. P., Dalca, A., Saglietti, L., Listgarten, J., & Fusi, N. (2018). Gaussian Process Prior Variational Autoencoders. Advances in Neural Information Processing Systems, 31.
[5] Casale, F. P., Rakitsch, B., Lippert, C., & Stegle, O. (2015). Efficient set tests for the genetic analysis of correlated traits. Nature Methods, 12(8), 755-758.