Aging is a biological process that limits the maximal lifespan of most organisms. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modelling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Nowadays, there are existing databases with access to longitudinal data (time series data). In addition, machine learning methods are increasingly commonly used to integrate different types of data.
In this project, the candidate aims to collect data from external databases such as UK Biobank and then integrate them into one database. It includes developing a generic pipeline for data extraction. Based on the integrated biological data and using AI techniques , the candidate will cluster data and asses the potential risk of disease. This task will include identification and assessment of biological datasets such as protein-protein interaction (STRING), pathway databases (KEGG, GO), and extraction of data from such databases.
It is required to have good programming skills (preferably in R or Python), understanding of data collection, visualization, and machine learning. Preferably, candidate has interest in biological aspect of data.
This project uses data science and machine learning to better understand how aging works. It’s a team effort from the Departments of Informatics, Laboratory Medicine, Pathology, and Microbiology.
If you want to learn more or get involved, please contact Diana Domanska, Pooya Zakeri, or Hilde Loge Nilsen.