## **Global Genes named three winners for the first challenge question which focused on expanding known phenotypes with previously unrecognized symptoms, including:** ### **? Best approach, combining RARE-X & external data: ** - 3Billion Team, led by Won Chan Jeong, Bioinformatics Engineer and Kyoungyeul Lee, Chief Scientific Officer; at 3Billion, Seoul, South Korea ### **? Best open source method to benefit rare disease research: ** - Chong Lab team, led by Jessica Chong, Ph.D., Assistant Professor in Pediatrics at the University of Washington, Seattle ### **? Most innovative approach to analysis of patient reported data:** - Systems Biomedicine Team at Marseille Medical Genetics, led by Anaïs Baudot, Ph.D., CNRS Director of Research ## **The second challenge question involved creating machine learning algorithms to predict disease diagnoses based on the diagnostic journey documented by families providing data. Global Genes named the following team as the winner of this challenge:** ### **? Best computational approach for predicting a diagnosis based on patient-reported data: ** - Ambit Inc.?s Data and Analytics Team led by Birnur Ozbas-Erdem, Ph.D., Vice President and Head of Analytics and Data Products at Ambit Inc. ## **The third challenge question focused on using data to validate or refute a potential therapeutic approach for one or more rare diseases. Global Genes announced two winners of this challenge:** ### **? Best use of the RARE-X data set: ** - Mefford Lab at St. Jude Children?s Research Hospital, led by Heather C. Mefford, M.D., Ph.D., Principal Investigator at St. Jude Children?s Research Hospital ### **? Most novel approach for potential therapeutic research: ** - Guan Lab at the University of Michigan, led by Kaiwen Deng and Yuanfang Guan, Ph.D., Associate Professor in the Department of Computational Medicine & Bioinformatics at the University of Michigan