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].
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 […]
2021Drug resistance mutations in HIV: new bioinformatics approaches and challenges., Curr Opin Virol 2021 Sep; 51(): 56-64.
2021Using machine learning and big data to explore the drug resistance landscape in HIV., PLoS Comput Biol 2021 Aug; 17(8): e1008873.
2020Origin, evolution and global spread of SARS-CoV-2, Comptes Rendus Biologies 2020; 20 p.
2020COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM., Bioinformatics 2020 Oct; (): .