Bruno Gabriel Nascimento Andrade

This paper evaluates genomic language models for bacterial classification using 16S rRNA sequences, showing they match or outperform BLAST+ and VSEARCH in species-level classification.

The paper identifies and characterizes putative colistin resistance genes (MCR) in the human gut microbiome using bioinformatics analysis of metagenome assembled genomes.

This study uses machine learning to analyze metagenomic data from the TARA Oceans project to identify and characterize antibiotic resistance genes (ARGs) in marine environments, revealing their abundance, distribution, and potential for horizontal gene transfer.