Evaluation of blood products in health and disease using machine learning and high throughput mass spectrometry

Evaluation of blood products in health and disease using machine learning and high throughput mass spectrometry


Our ultimate goal is to develop and transfer new procedures for quality control and diagnostics employing high throughput mass spectrometry and machine learning approaches to industry. Our software solution coupled to detailed protocols for sample preparation could be adapted to most complex biological products and assist in classifying specific states in health and diseases. This project is highly disruptive since it uses very high throughput mass spectrometry enabled by the laser diode thermal desorption process capable of testing thousands of samples in a day at a fraction of the cost of traditional approaches.

Moreover, machine learning algorithms allow to characterize these complex biological matrices to an unprecedented level by literally monitoring hundreds of thousands of molecular entities in seconds and identifying minimally the best ones that can characterize a specific state facilitating the development of specific assays. We will train highly qualified personnel that will have employment opportunities with our partners; Phytronix and Waters are in the field of clinical mass spectrometry at large. 

Investigators


Jacques Corbeil

CHUdeQc/Université Laval

François Laviolette

Université Laval

Mario Marchand

Université Laval

Funding


mitacs1