Basirudeen Syed Ahamed Kabeer

This paper develops a framework using large language models for gene prioritization, validated against literature, identifying 30 high-confidence therapeutic candidates for sepsis.

This study profiles the transcriptional responses to three mRNA vaccine doses, showing sustained polyfunctional immunity after a nine-month interval.

This paper introduces a method using Large Language Models to perform deep functional profiling of gene sets, demonstrating its ability to uncover detailed immune functions and improve upon traditional annotation tools.

This paper develops an API-driven workflow using GPT-4 to automate candidate gene prioritization, demonstrating high consistency and scalability in identifying sepsis-related genes.

The paper investigates NUDT16 transcript abundance in sepsis using omics data and public datasets, finding elevated levels in septic patients compared to controls.