Luc Blassel’s PhD project, funded by PRAIRIE, is to apply machine learning techniques to the analysis of sequence data. Traditional sequence alignment and analysis methods might not be able to perform as well as expected, with the ever growing amount of high quality sequence data. One of the goals of this project is to use deep learners to contextualise aligners. We aim for this method to allow for more accurate alignments, which are at the heart of all comparative biology analyses. Luc also aims to use machine learning directly for sequence analysis, and is applying this approach to the study of the drug-resistance landscape in HIV-1. He will also apply similar methods to study the impact of HIV-1’s ASP gene on virulence [Cassan et al. PNAS 2016].
INCEPTION – Institut Convergence for the study of Emergence of Pathology Through Individuals and Populations
IINCEPTION Goal The Inception’s goal is to develop a core structure to mobilize data resources, numerical sciences, and fundamental experimental biology in a range of health issues (Official website here : https://www.inception-program.fr/en). Inception program […]
Applying machine learning to sequence analysis (PhD project)
Luc Blassel’s PhD project (funded by PRAIRIE – supervision O. Gascuel) is to apply machine learning techniques to the analysis of sequence data. Traditional sequence alignment and analysis methods might not be able to […]
HIV-1 Drug Resistance Mutation analysis
HIV-1 drug resistance mutations (DRMs) can be dangerous not only on the individual level but also for the whole population. Acquired drug resistance (ADR) appears under drug selective pressure and limits the choice of […]