Machine learning-driven identification of critical gene programs and key transcription factors in migraine.

ABSTRACT:

Background

Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis.

Methods

We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each re

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11745026 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature