Bioinformatics / machine learning

Bioinformatics / machine learning

The digital revolution is profoundly transforming biomedical research. Bioinformatics and machine learning now make it possible to analyze massive volumes of biological data, extract complex patterns, and develop predictive tools with unmatched precision. These approaches pave the way for a deeper understanding of the biological mechanisms underlying reproductive health, helping to accelerate the development of new strategies for prevention, diagnosis, and treatment. Within the RQR, several researchers are integrating these advanced technologies to advance our knowledge in the field of reproductive science.

Juan Carlos Arango Sabogal applies machine learning techniques and Bayesian statistical models to optimize the detection, prevention, and control of infectious diseases in dairy cattle and horses, thereby strengthening reproductive health and herd sustainability.

Simon Dufour develops data visualization and analysis tools to monitor antimicrobial use and investigate the causes of fetal loss in cattle, particularly through the analysis of biomarkers such as pregnancy-associated glycoproteins.

Kevin Wade specializes in artificial intelligence applied to dairy production, using neural networks, decision trees, and big data analytics to predict dairy cow lifespan and disease incidence, with a focus on profitability and prevention.

Abdoulaye Baniré Diallo leads projects at the intersection of bioinformatics, single-cell biology, and AI. His lab designs algorithms  and methods for the integration and analysis of heterogeneous biological data.

Nicolas Gévry combines functional genomics, systems biology, and bioinformatics to investigate gene regulation involved in reproduction and hormone-related cancers. His team studies nuclear receptors and their influence on gene expression using high-resolution omics approaches.

Sébastien Buczinski helps bridge the gap between research and on-farm application by integrating field data into practical decision-making tools aimed at improving animal health and productivity.

The integration of bioinformatics and artificial intelligence into RQR projects marks a major turning point in the study of animal and human reproduction. By combining large-scale data, advanced modeling, and biological expertise, these approaches enable a better understanding of the complex interactions between genes, environment, and individuals, in a perspective of global and sustainable health.