Lisa Brooks, Ph.D., program director in the NHGRI Genomic Variation Program talks about the 1000 Genomes Project and two studies published online on Sept. 30, 2015 in Nature, detailing how an international team of scientists examined the genomes of 2,504 people from 26 populations across Africa, East and South Asia, Europe and the Americas. More info: http://www.genome.gov/27562711

What if you could pick out early warning signs of heart conditions out of somebody’s Fitbit data? It turns out that you can.

This technology was developed by Mike Klein, a neuroscience Ph.D out of McGill University, as part of the Insight Health Data Science Fellowship. Insight offers a Fellowship three times a year where academics learn the applied data science skills they need to work in industry. Klein had already used machine learning in his research – it comes in handy when interpreting fMRI data – but at Insight he picked up industry standard tools and data science workflow to work with messy dataset. As a long-time wearable user, Klein was fascinated by the Health eHeart study at UCSF where over 30,000 participants have contributed their Fitbit step data and clinical information (anonymized) to help study heart disease. After many hours of wrangling and feature engineering, Mike was able to predict early warning signs of heart conditions based on walking patterns extracted from Fitbit records.
Source: The Emerging Field of Health Data Science