Simon S. McDade

This study identifies mitochondrial metabolism as a key factor in 5-fluorouracil resistance in colorectal cancer and shows that targeting mitochondrial Complex I can enhance chemotherapy effectiveness.

This paper introduces a neuro-symbolic framework that autonomously prioritizes colorectal cancer vulnerabilities using multi-scale AI swarms and clinical data, identifying IGF2 as a predictive biomarker for 5-Fluorouracil sensitivity.

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

This study uses high-resolution spatial proteomics to characterize colorectal cancer consensus molecular subtypes at single-cell resolution, revealing subtype-specific immune and metabolic features.

This study shows that 5-Fluorouracil induces ribosome collisions, which are mitigated by the RQC mechanism through mTOR-dependent regulation of RQC factors.

The paper investigates the spatial relationship between T cells and cancer cells in stage III CRC patients, finding that closer proximity of T cells to cancer cells is associated with better disease-free survival.

MouSR is an open-source tool for rapid transcriptomic analysis of human or mouse data, enabling biological discovery without programming expertise.

The paper identifies p63 binding sites and predicts its gene regulatory network using ChIP-seq data and motif analysis, highlighting potential therapeutic targets in SCC.

ACE identifies correlations between clinical and genomic data in TCGA using an evolutionary algorithm for efficient analysis.