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Biomarkers of aging have been notoriously difficult to find. In this Build Project, you will analyze real-world biological data to find good ones.
As long as humans have existed, they have tried to live longer. But today, the combination of large-scale ‘omics data and machine learning may help us unlock some of the secrets behind why we get old, and what we can do about it.
In this Build Project, you'll assume the role of a Computational Biologist, analyzing methylomics data to identify novel biomarkers of aging. During the Build Project, we will cover the theory behind the field of aging biology, which you will then use to build models. You will retrieve, clean, and process omics data, apply machine learning models for data analysis, and produce scientific figures. It is assumed that you have familiarity with biology OR data science (ideally both). If you don’t have experience with one of them, you can still enroll but you will need to learn on your own to get the most out of the project.
Introduction to the data science challenge that you’ll be participating in during the Build Project.
What defines aging at a cellular level? What processes are involved? Can we understand the framework of hallmarks of aging, and can we poke holes in this framework?
Is aging pre-programmed, just like a piece of software?
Checking in on the progress you’re making, and helping out with the challenge wherever possible.
Can we reprogram cells to a younger, healthier state?
Learn how this promising approach of screening potential drugs works.
What approaches are companies in the longevity space trying?
You will present your findings and reflect on your learnings throughout this project.
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