There is a massive amount of data in the medical field that could be put to use. Think of patient charts in hospitals, RAMQ data, and population databases that record lifestyle habits, living conditions, as well as medical and genetic data. Going beyond genetics, the focus today is on proteomics, metabolomics and other “omics” that are bringing their share of data on proteins, metabolism and so on.
Julie Hussin is a researcher at the Montréal Heart Institute (MHI) and leads a computational biomedicine laboratory. She discusses the database being developed by MHI that will eventually contain over 30,000 participants. She also mentions CARTaGENE, the Québec biobank containing data on 43,000 people, and the UK Biobank with 500,000 participants.
If these data could speak, they could support preventive medicine. For example, an algorithm derived from heart attack victim data would learn to identify the distinctive features of the heart attack and who is at risk for one. It could also help establish a diagnosis, for example, by specifying the type of breast cancer. It could even support personalized medicine by suggesting a treatment tailored to the patient’s genetic profile. Everyone reacts differently to treatments, and algorithms would help match the right treatment to the right patient.
Beyond the medical applications, there is still the fundamental aspect, for which data exploration would enable a better understanding of the physiological processes of the various pathologies.
Developing such algorithms to create value and make use of all the data building up in banks is the main focus of Julie Hussin’s laboratory work.
It’s crazy how much biomedical data is underused due to a lack of methodologies to be able to gain from it. This leaves room for a lot of discoveries!”
- Proteomics: the study of the full set of proteins encoded by a genome.
- Metabolomics: the study of the set of metabolites present within an organism, cell or tissue.