Xing‐Ming Zhao

The paper uses long-read metagenomic sequencing to analyze urban soil samples, revealing novel microbial lineages and extensive functional diversity.

The paper improves brain age estimation using an adversarial convolution network and identifies BAG-associated genes and regulatory networks linked to brain disorders.

AEMB improves metagenomic binning efficiency by reducing computational demands while maintaining binning accuracy.

The paper constructs a global microbial small ORFs catalogue and identifies a tool for annotating small proteins in microbial genomes.

The paper identifies four stable fungal enterotypes in the human gut mycobiome, showing age preference and disease associations.

The paper uses long-read sequencing to show that most gut phages are methylated, with many encoding their own methyltransferases, suggesting methylation helps phages evade host defenses.

SemiBin2 improves metagenomic binning by using self-supervised learning, outperforming previous methods in both short- and long-read sequencing.

metaMIC identifies and corrects misassemblies in metagenomic assemblies, improving downstream scaffolding and binning results.

The paper develops a method combining phage enrichment and long-read sequencing to recover more complete gut phage genomes from fecal samples, identifying a megabase phage and revealing hidden diversity.

SemiBin improves metagenomic binning by integrating reference genomes with semi-supervised deep learning, producing higher-quality bins with greater taxonomic diversity in simulated and real datasets.

The paper identifies 11,285 genetically interpretable comorbidities among 439 diseases using UK Biobank data, showing shared genetic components and hub diseases involved in comorbidity networks.

This study predicts Alzheimer's disease-associated genes using brain gene networks and identifies causal variants, providing a resource for therapeutic target discovery.

MACREL identifies high-quality antimicrobial peptide candidates from genomes and metagenomes using a novel set of peptide features and classifiers.

This study identifies age-specific gene signatures in the human cerebral cortex using TCA, linking them to structural features and neuropsychiatric disorders.

nMAGMA improves risk gene annotation from GWAS summary statistics by integrating regulatory networks and tissue-specific signals, identifying more schizophrenia-related genes than existing methods.

The paper identifies human gut microbes that undergo consistent changes after transplantation into germ-free mice, highlighting challenges in replicating human microbiota phenotypes in mouse models.