Mohammad Nazmus Sakib

The paper designs and evaluates VP4-targeting siRNAs using computational methods to identify an optimized siRNA with improved structural compatibility and stability.

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 designs hyperglycosylated analogs of human erythropoietin using computational methods and identifies Analog-71.1 as a promising candidate with strong receptor interaction and structural stability.

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.