🏆 Congratulations to our Biomarkers of Aging Challenge Phase 1: Chronological Age Prediction winners!
Our participants showcased remarkable predictive accuracy, achieving an average error of less than 3 years.
🥇 1st Place: Julian Reinhard, also known as “DarthVenter,” Machine Learning Scientist at Evotec, achieved a final score of 2.45 years age error. Julian will receive $15,000 USD in cash prizes!
🥈 2nd Place: Lucas Paulo de Lima Camillo, Head of Machine Learning at Shift Bioscience, achieved a final score of 2.55 years age error. Lucas will receive $10,000 USD in cash prizes!
🥉 3rd Place: Team “ZetaPartition”, comprising academics Jakob Träuble and Stefan Jokiel, achieved a final score of 2.46 years age error. The team will receive $5,000 USD in cash prizes!
Check out our Age Prediction Leaderboard on Synapse for the final rankings here:
synapse.org/Synapse:syn52966…
ℹ About Our Challenge & Why Evaluate Chronological Age:
The Biomarkers of Aging Challenge aims to generate and benchmark the best prediction models for chronological age, mortality, and multi-morbidity.
📊 The challenge leverages a unique, high-quality dataset that includes DNA methylation and aging outcome data for over 500 diverse individuals. DNA methylation and other first-generation biomarkers of aging are often trained to predict chronological age. Deviations between predicted and actual age (prediction errors) can indicate 'higher' or 'lower' biological age, which has been linked to age-related health outcomes, including mortality.
Phase 2 of our Challenge, evaluating mortality, has now launched. You can join Phase 2:
synapse.org/Synapse:syn52966…
Once again, congratulations to our winners, and thank you to all participants for their outstanding contributions!