Seminar: How Bioinformatics Research Can Be Used to Reduce Racial Inequity Toward Underrepresented Groups?

Abstract

The Human Genome Project has given the world a deeper understanding of our DNA and allowed us to have a better knowledge of the biological processes such as Alternative Splicing (AS). AS is a biological process by which a eukaryotic gene can diversify its functionality by producing several ribonucleic acids and proteins. One of the most remarkable features of this process is that more than 94% of human multiexon genes undergo AS. Moreover, various human diseases such as cancer, autism, and some neurological diseases are directly related to the AS dysfunction.

In this presentation, I will start by introducing the methods to reconstruct gene and protein trees. Subsequently, I will present the double reconciliation problem, and the clustering problem we introduced in the context of alternative splicing. Then I will present two ongoing works: the first one is a probabilistic graphical model used to predict the impact of mutations on alternative splicing. The second one is a machine learning models used to analyze medical records related to Aortic Stenosis diseases.

Even if current genomic research has led to numerous advances in our understanding of AS, most of this research is conducted on a non-diverse population. This has the consequence to intensify health disparities by promoting discoveries that will disproportionately benefit well-represented populations. I will present as future research my plan to address the issues of inequities against underrepresented ethnic groups. That will allow these groups to benefit from research results and to contribute to addressing health problems specific to them.

Bio

Esaie Kuitche Kamela received his Ph.D. degree from the Université de Sherbrooke, Canada in 2020. He is now a postdoctoral in the school of computer science at the University of McGill, Canada, where he is developing models and methods to dissect the AS mechanisms and to analyzing medical records. His research interests mainly reside in the intersection of computational biology, algorithms, and machine learning.