Luca Freschi

This paper presents a deep convolutional neural network to predict antibiotic resistance in M. tuberculosis isolates, achieving state-of-the-art accuracy and enabling interpretable feature analysis.

The paper identifies genome-wide mutational dependencies in Mycobacterium tuberculosis evolution, showing how mutations interact to confer antibiotic resistance and detect environmental pressures.

GenTB predicts tuberculosis drug resistance from genomic data using machine learning, achieving higher sensitivity than existing tools with slightly lower specificity.

The paper demonstrates epistatic interactions in Mycobacterium tuberculosis complex that can reverse antibiotic resistance, specifically showing that eis promoter mutations cannot confer resistance if paired with LoF mutations in the eis coding region.

This study analyzes global M. tuberculosis whole genome sequences to trace the temporal and geographic patterns of drug resistance acquisition and its correlation with country wealth.

The paper analyzes M. tuberculosis population dynamics during treatment failure using whole-genome sequencing data from clinical isolates to identify resistance mutations and in-host evolutionary patterns.

This study uses a multitask deep learning model to predict tuberculosis drug resistance from genomic data, achieving high accuracy in both first-line and second-line drug resistance prediction.