Christian M. Boßelmann

This study evaluates the underutilization of syndrome-specific ICD-10 codes for monogenic epilepsies in clinical practice, finding that less than two-thirds of patients with molecular diagnoses were assigned these codes.

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.

This study creates an age-aware genotype-phenotype atlas for 87 neurodevelopmental disorders using data from the Simons Searchlight cohort, identifying gene-specific risk profiles and age-dependent penetrance patterns.

This paper predicts drug response in epilepsy using multi-task gradient-boosted trees, identifying key genomic and clinical features that influence treatment outcomes.

This study analyzes 586 DEPDC5 variant carriers to estimate penetrance, phenotypic features, and treatment outcomes in DEPDC5-related epilepsy.

This study identifies shared and distinct ultra-rare genetic risk factors for epilepsy subtypes using whole-exome sequencing of 20,979 patients and 33,444 controls.

This study identifies clinical features preceding genetic epilepsy diagnosis in EMR data from 32,000 individuals, showing early signs can inform earlier treatment.

The paper develops a Bayesian framework to integrate functional evidence into clinical variant interpretation for epilepsy-related ion channel genes, identifying key parameters that influence pathogenicity classification.

The paper uses natural language processing and phenome-wide analysis to identify tachycardia as a significant comorbidity in CDKL5 deficiency disorder.

The paper develops a machine learning framework that uses phenotypic similarity to predict the functional effects of missense variants in voltage-gated sodium channels, achieving high accuracy and robustness.

This paper develops a multi-task learning model to predict the functional effects of voltage-gated potassium channel variants, achieving improved accuracy over standard methods.

The paper identifies FGF12 variants linked to DEE and ASD and shows their functional effects on NaV1.2 and NaV1.6 sodium channels, revealing complex gain- and loss-of-function changes.