| Title: | Development of Machine Learning-based Techniques for Biological Age Prediction |
| Lead Institution: | Oxcitas Limited |
| Principal investigator: |
Application 88003
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About
Our mission is to make use of the latest discoveries in Machine Learning (ML) as well as of different in-house developed methodologies to build a variety of biological age clocks mainly constructed from blood and epigenetics biomarkers. These models will ultimately allow us to have a better understanding of the ageing process, which is a key problem as it is known that 65+ years of age population is likely (>53%) to suffer from a chronic disease any time soon. Among other ramifications, this implies that such a demographic group accounts for more than half of social care spending worlwide. Taking this into account, our goal is to quantify the effect of different biomarkers on the ageing process and combine such findings with additional models, which correlate the temporal evolution of these biomarkers with respect to diets and exercise routines, Combining both methodologies, we then plan to develop a tool for people to understand the effect of lifestyle in the evolution of biological age, and hence, analyse the implications of lifestyle changes to decelerate the ageing process. We expect to have publications in this regard within the next 3 years.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14472 | UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases | Marco Capó (+7) | 2025 | NeuroImage |
Enabling scientific discoveries that improve human health