scGRNom

scGRNom is a computational pipeline developed to illuminate the complexities of cell-type-specific gene regulatory mechanisms, from genetic variants to diseases. Its capacity to predict disease genes and their regulatory networks, including transcription factors and regulatory elements tailored to specific cell types, stands out. Demonstrating its utility, scGRNom has been applied to neurological conditions such as schizophrenia and Alzheimer's disease, successfully identifying disease genes and regulatory networks for key brain cell types like excitatory and inhibitory neurons, microglia, and oligodendrocytes.

Beyond gene and network predictions, scGRNom facilitates enrichment analyses, uncovering shared and unique functions and pathways associated with diseases at the cell-type level. An innovative aspect of scGRNom is its use of machine learning to demonstrate that incorporating cell-type disease genes can enhance the prediction of clinical phenotypes, offering new avenues for understanding and potentially addressing complex diseases.