Ishtiaque Ahammad

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

The paper introduces APDeeM, an ensemble machine learning system for identifying antiviral peptide candidates, achieving high accuracy and efficiency compared to existing methods.

The paper uses animal models and in silico experiments to identify antidiabetic compounds in Ficus racemosa extracts, finding four compounds with potential therapeutic effects.

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.

The paper identifies shared genes across different breast cancer etiologies and suggests potential therapeutic targets through transcriptomic analysis and computational modeling.

The paper identifies fifteen PTEN mutations in lung cancer, seven of which significantly disrupt structure and function, supported by computational analysis and molecular simulations.

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.

This study investigates the anti-diabetic potential of Mitragyna speciosa using in vivo and in silico methods, finding that extracts reduce blood glucose levels and four compounds bind effectively to the DPP4 receptor.

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 investigates nsSNPs in the Drd2 gene, identifies F389V as the most impactful variant, and shows its altered binding properties with dopamine and risperidone.

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 identifies 6 key proteins associated with myocardial infarction using gene expression data and systems biology approaches.

The paper performs GSEA on upregulated genes in cocaine addiction and identifies three miRNAs with therapeutic potential.

The paper identifies key proteins involved in axon guidance disorders using systems biology tools and analyzes their molecular mechanisms.

The paper analyzes protein-protein interaction networks and pathways related to nor-NOHA and NOHA to identify key proteins involved in arginase inhibition and cancer treatment.