Mohammed Toufiq

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

This paper identifies a three-gene interferon signature that predicts sustained remission in pediatric AML patients by stratifying chemosensitivity and survival outcomes.

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 provides R scripts for analyzing and visualizing blood transcriptional module repertoires, demonstrating their application in septic patients' transcriptome data.

The paper analyzes imprinted gene expression in mouse skin, finding elevated expression of 16 IGN genes during telogen and early anagen phases and suggesting they regulate hair cycle-associated genes.

The paper investigates FAM129A expression in neutrophils exposed to sepsis serum using transcriptome data and confirms surface expression through bench validation.

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