Simone Marini

Vir2vec is a genome-wide viral embedding model that outperforms existing models in viral genome understanding tasks and provides explainability for host adaptation mutations.

AURA is a scalable machine learning framework for predicting antibiotic resistance in Mycobacterium tuberculosis, identifying novel resistance-associated loci.

SARITA is a large language model that generates synthetic SARS-CoV-2 Spike S1 sequences, accurately predicting mutations in future variants like Delta and Omicron.

The paper develops a high-content screen to identify effective Pseudomonas aeruginosa phages and identifies host genes influencing phage specificity.

This paper introduces DeepAutoCoV, a deep learning system that predicts future dominant SARS-CoV-2 lineages by detecting anomalies in genomic data, achieving better performance than baseline methods with early detection of emerging variants.

This paper develops a machine learning classifier to predict antibiotic resistance in Mycobacterium tuberculosis using genomic data and achieves high accuracy for first-line antibiotics.

OCTOPUS is a metagenomics classifier for portable sequencing data that improves accuracy and reduces false positives on mobile devices.

The paper introduces DeepDynaTree, a phylogenetic-informed graph deep learning model to classify dynamic transmission clusters in infectious disease epidemics, achieving higher accuracy than existing methods.

BrainCellR provides a standardized cell type nomenclature system for consistent classification and comparison across brain single-cell datasets.

KARGVA identifies antibiotic resistance gene variants from metagenomics data with high accuracy and efficiency, outperforming existing tools in detecting ARGVs.

The paper evaluates AMR prediction methods using HTS data from clinical isolates, finding high variance in performance and highlighting issues with sampling bias and uncharacterized AMR genes.

TARDiS optimizes viral genome subsampling by balancing genetic diversity and temporal distribution to improve phylogenetic analysis.

DYNAMITE identifies dynamic transmission clusters in viral phylogenies by analyzing branches rather than clades, outperforming existing tools in simulated outbreaks.

This study analyzes how global and regional public health interventions affected SARS-CoV-2 transmission clusters using genetic sequence data and phylogenetic analysis.

The paper presents a matrix factorization approach for protease target prediction that integrates diverse biological data and outperforms existing methods.