Ahmed H. Ghobashi

The paper constructs a multi-omics single-cell atlas of human RPE and choroid, identifying cell types, aging patterns, and genetic risk factors for AMD.

SpaFlow models ligand-receptor interactions in spatial transcriptomics data by simulating diffusion and binding processes, revealing spatially coherent signaling patterns in multiple disease contexts.

The paper integrates single-cell RNA sequencing and spatial transcriptomics to identify CTHRC1+ fibroblasts as key fibrogenic cells in IPF, showing how culture affects their transcriptional profile.

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

The paper uses single-cell multi-omics to study EEC differentiation in mucinous colorectal cancer, identifies STAT3 as a regulator, and discovers a rare cancer cell type with neuron-like features.

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.

This study shows that goblet cell content influences CMS3 subtype classification in colorectal cancer using machine learning.