Machine learning classification meets migraine: recommendations for study evaluation.

The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility.

A preliminary study of the miRNA restitution effect on CNV-induced miRNA downregulation in CAKUT.

Background: The majority of CAKUT-associated CNVs overlap at least one miRNA gene, thus affecting the cellular levels of the corresponding miRNA. We aimed to investigate the potency of restitution of CNV-affected miRNA levels to remediate the dysregulated expression of target genes involved in kidney physiology and development in vitro.

Methods: Heterozygous MIR484 knockout HEK293 and homozygous MIR185 knockout HEK293 cell lines were used as models depicting the deletion of the frequently affected miRNA genes by CAKUT-associated CNVs.

Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data.

Background: Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field.