Marco Salemi

The study identifies immunosuppressive monocytes predicting SIV encephalitis by analyzing their transcriptional changes during infection.

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

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.

SEHI-PPI predicts human-influenza protein-protein interactions using a double-view deep learning framework with adaptive negative sampling, outperforming existing methods and demonstrating strong generalizability.

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.

DeepDynaForecast uses phylogenetic trees and graph deep learning to predict epidemic transmission dynamics, showing accuracy in simulated and real HIV data.

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.

The paper uses single-cell DNA sequencing to show that defective HIV genomes contribute to phylogenetic inference of reservoir dynamics in an animal model.

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 uses molecular dynamic simulations to analyze VP1 mutants of Rotavirus group A, revealing structural changes that may affect viral replication and vaccine effectiveness.

This study shows that HIV-1 evolves differently in monocytes compared to T cells, suggesting monocytes may be a significant viral reservoir.