Rikke Steensbjerre Møller

The paper introduces Gene Portals, a framework that integrates clinical, functional, and structural data for rare disease variant classification, demonstrating its utility across five gene-specific portals.

The paper investigates how genetic variants affecting transcription factor binding contribute to valproate-induced birth defects and identifies EP300 as a key regulator involved in this process.

The paper uses molecular dynamics simulations to analyze four SCN2A variants, revealing how they affect channel structure and function, with R937C being the most unstable.

The paper identifies biological properties of ion channel residues associated with variant pathogenicity and links them to clinical phenotypes and functional assays.

The paper reports a cohort of 17 patients with pathogenic KCNH5 variants causing neurodevelopmental disorders and epilepsy, identifying genotype-phenotype correlations.

This study identifies genetic loci associated with impulsivity in juvenile myoclonic epilepsy and links them to synaptic assembly genes and SLCO5A1 function.

CNV-ClinViewer provides a web tool for clinical interpretation of large copy-number variants by integrating patient and population data with biomedical annotations.

The paper investigates the functional consequences of SLC6A1 variants, finding haploinsufficiency underlies neurodevelopmental phenotypes and identifying missense residues sensitive to substitution.

The paper analyzes GABRB3 variants in a cohort of 71 individuals to identify genotype-phenotype correlations, finding associations between variant locations and specific clinical features.

The paper reclassifies DYRK1A variants using an integrative approach, showing some previously reported pathogenic variants do not cause obvious phenotypes.

This study describes 20 individuals with de novo SNAP25 variants, identifying intellectual disability and early-onset epilepsy as core symptoms of SNAP25-DEE, with overlaps with other SNARE complex genes.

The paper predicts functional effects of missense variants in voltage-gated sodium and calcium channels using machine learning, achieving high accuracy in distinguishing gain and loss of function variants.

The paper identifies de novo variants in neurodevelopmental disorders with epilepsy and finds associations with specific genes and clinical features.

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.

The paper develops a paralog conservation score to identify disease-associated genes and shows that disease variants are enriched at conserved sites across gene families.