Research Interests
Preprint
Sophie Wharrie, Samuel Kaski, Meta-Learning With Hierarchical Models Based on Similarity of Causal Mechanisms, arXiv preprint, 2024, https://arxiv.org/abs/2310.12595
Journal/conference publication
Sophie Wharrie, Zhiyu Yang, Andrea Ganna, Samuel Kaski. (2023). Characterizing personalized effects of family information on disease risk using graph representation learning. Proceedings of the 8th Machine Learning for Healthcare Conference, New York, USA, in Proceedings of Machine Learning Research (PMLR), 219:824-845. https://proceedings.mlr.press/v219/wharrie23a.html
Journal/conference publication
Sophie Wharrie, Zhiyu Yang, Vishnu Raj, Remo Monti, Rahul Gupta, Ying Wang, Alicia Martin, Luke J O’Connor, Samuel Kaski, Pekka Marttinen, Pier Francesco Palamara, Christoph Lippert, Andrea Ganna, HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes, Bioinformatics, Volume 39, Issue 9, September 2023, https://doi.org/10.1093/bioinformatics/btad535
Journal/conference publication
Sophie Wharrie, Lamiae Azizi, Eduardo G. Altmann, Micro-, meso-, macroscales: The effect of triangles on communities in networks, Physical Review E, Volume 100, Issue 2, August 2019, https://link.aps.org/doi/10.1103/PhysRevE.100.022315
Workshop paper
Sophie Wharrie, Zhiyu Yang, Vishnu Raj, Remo Monti, Rahul Gupta, Ying Wang, Alicia Martin, Luke J O’Connor, Samuel Kaski, Pekka Marttinen, Pier Francesco Palamara, Christoph Lippert, Andrea Ganna, HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data, NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, New Orleans, USA, 2022
Journal/conference publication
Remo Monti, Lisa Eick, Georgi Hudjashov, Kristi Läll, Stavroula Kanoni, Brooke N Wolford, Benjamin Wingfield, Oliver Pain, Sophie Wharrie, Bradley Jermy, Aoife McMahon, Tuomo Hartonen, Henrike O Heyne, Nina Mars, Genes & Health Research Team, Kristian Hveem, Michael Inouye, David A van Heel, Reedik Mägi, Pekka Marttinen, Samuli Ripatti, Andrea Ganna, Christoph Lippert, Evaluation of polygenic scoring methods in five biobanks reveals greater variability between biobanks than between methods and highlights benefits of ensemble learning. The American Journal of Human Genetics, 2024.