HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data
Sophie Wharrie ⋅ Zhiyu Yang ⋅ Vishnu Raj ⋅ Remo Monti ⋅ Rahul Gupta ⋅ Ying Wang ⋅ Alicia Martin ⋅ Luke O'Connor ⋅ Samuel Kaski ⋅ Pekka Marttinen ⋅ Pier Palamara ⋅ Christoph Lippert ⋅ Andrea Ganna
2022 Poster
in
Workshop: Synthetic Data for Empowering ML Research
in
Workshop: Synthetic Data for Empowering ML Research
Abstract
In this extended abstract we present a new highly efficient software tool called HAPNEST that enables machine learning practitioners to easily generate and evaluate large synthetic datasets for human genetics applications. HAPNEST enables the generation of diverse synthetic datasets from small, publicly accessible reference datasets. We demonstrate the suitability of HAPNEST-generated data for supervised tasks such as genetic risk scoring.
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