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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

Fri Dec 02 07:30 AM -- 07:32 AM (PST) @
in Synthetic Data for Empowering ML Research »
Event URL: https://openreview.net/forum?id=zW6XoC-v6_D »

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.

Author Information

Sophie Wharrie (Aalto University)
Zhiyu Yang
Vishnu Raj (Aalto University)
Remo Monti
Rahul Gupta
Ying Wang
Alicia Martin
Luke O'Connor
Samuel Kaski (Aalto University and University of Manchester)
Pekka Marttinen (Aalto University)
Pier Palamara (University of Oxford)
Christoph Lippert (Hasso Plattner Institute for Digital Engineering, Universität Potsdam)
Andrea Ganna

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