Mohammad Uzzal Hossain

This study uses whole-genome sequencing and pan-genome analysis of Bangladeshi diarrheal pathogens to identify core genes involved in pathogenesis and antibiotic resistance.

A meta-analysis of bulk RNA-seq datasets identifies 12 differentially expressed genes in Alzheimer's disease, with TTR showing potential as a biomarker and suggesting Levothyroxine as a repurposable therapeutic.

This paper develops AITeQ, a machine learning framework for Alzheimer's disease prediction using a 5-gene signature identified from RNA-Seq data.

This study compares the gut microbiome of Bengali and indigenous ethnicities in Bangladesh, finding distinct microbial profiles and phylosymbiotic links with Indian groups.

This study analyzes 100 whole genomes of diarrheal pathogens, including Plesiomonas shigelloides, to identify coinfection patterns and shared hypothetical proteins with other diarrheal bacteria.

This study explores the genetic mechanisms of Providencia stuartii in microbial interactions with diarrheal pathogens and identifies potential therapeutic targets.

The paper investigates the genomic features of C. werkmanii strain NIB003, identifying antibiotic resistance genes, virulent genes, and genetic variation that may contribute to disease pathogenesis.

This study identifies potential biomarkers for Parkinson's disease using blood-based gene expression data and develops a machine learning classifier called PitArray for non-invasive detection.

This study designs a pan-Salmonella multi-epitope vaccine using subtractive proteomics and immunoinformatics, identifying CsgA as a conserved vaccine candidate with strong immunogenic properties.

This study compares antibiotic resistance gene profiles in Bangladeshi gut microbiomes to global samples, finding higher ARG abundance in Bangladeshi cohorts.

The paper identifies novel mutations in SARS-CoV-2 NIB-1 genome and analyzes their impact on NSP1 and PLPro proteins for therapeutic development.

This study evaluates the susceptibility and fatality of lung cancer patients to COVID-19 by analyzing ACE2 and CXCL10 expression and their co-expressed genes in lung cancer subtypes.