Yousong Peng

This paper introduces vhPPIpred, a machine learning method for predicting virus-human PPIs that outperforms existing methods and provides a benchmark dataset.

This study shows that influenza infection in juvenile mice causes lifelong multi-organ damage and Parkinsonian changes in aging, with persistent inflammation and secreted proteins mediating systemic effects.

This study identifies 5,053 novel virus long non-coding RNAs (vlncRNAs) across 25 viral species and finds that many structurally mimic host lncRNAs, potentially acting as miRNA sponges.

This study identifies LRP8 as a receptor for Yellow Fever Virus, showing it facilitates infection and presents a potential therapeutic target.

This paper introduces FluRisk, a computational framework using genomic data and AI to assess the risk of emerging influenza viruses, showing improved performance over traditional methods.

This study analyzes the virome in SLE patients and controls, finding higher viral positivity in patients and suggesting viral infections may worsen disease severity.

This study models the antigenic evolution of seasonal influenza viruses using PREDAC and finds no need to update vaccine strains for the 2025 season.

This study characterizes integrin family alterations across 32 cancer types, identifies integrinScore as a prognostic marker, and proposes potential therapeutic targets.

VIGA is a tool for eukaryotic virus identification and genome assembly from NGS data, showing improved performance in assembling complete genomes and revealing virome differences in disease states.

vsRNAfinder identifies high-confidence viral small RNAs from sRNA-Seq data, outperforming existing methods in sensitivity.

This study develops a random forest model to predict coronavirus 3CL protease cleavage sites, achieving high accuracy on both viral and host protein datasets.

This paper introduces PHP, a Gaussian model for predicting hosts of prokaryotic viruses in metagenomics, achieving higher accuracy than existing methods.

The paper predicts the receptorome for the human-infecting virome using a random-forest model and protein-protein interaction data, identifying 1380 human cell membrane proteins and 693 virus-specific receptors.

The paper predicts antiviral drugs against African Swine Fever Viruses by analyzing protein-protein interactions and identifying key host proteins as potential drug targets.

This study classifies MGF proteins of ASFV into 35 groups based on sequence homology and provides a web server for classification.

This study develops machine learning models to identify human-infecting viruses using k-mer frequency in viral genomic sequences, achieving over 90% accuracy.

This study analyzes ASFV genomes to show that homologous recombination contributes to genetic diversity through indels, with conserved enzymes involved in recombination.

The paper updates an epidemiological model to predict influenza incidence for the 2018-2019 season and evaluates its accuracy for the 2017-2018 season.