Jozef Gécz

This paper introduces KIML, a machine learning framework that integrates experimental data with biological knowledge to improve disease gene prioritisation, showing better performance than existing methods.

The paper identifies a chimeric CLMN::SYNE3 RNA transcript from a 14q32.13 duplication as the cause of SCA30, a form of cerebellar ataxia, through RNA-Seq and functional studies.

This study uses TMT-based proteomics to analyze the effects of BCA and GBCA on SH-SY5Y cells, identifying distinct dysregulated proteins and pathways associated with neurotoxicity.

This study evaluates optical genome mapping (OGM) for detecting STR repeat expansions and somatic instability, showing high accuracy and efficiency compared to current methods.

The paper identifies a novel FTSJ1 pathogenic variant and shows its role in neuron morphology and learning performance through RiboMethSeq and transcriptome analysis in human and Drosophila models.

The paper systematically analyzes chrX genes to predict disease associations, identifying 235 genes likely involved in disorders and reporting damaging variants in CDK16 and TRPC5 in patients with intellectual disability or autism.

The paper surveys rare epigenetic variation in 23,116 human genomes, identifies disease-relevant epivariations, and discovers novel CGG expansions linked to folate-sensitive fragile sites.

The paper shows that common genetic variants contribute to risk and clinical variation in severe neurodevelopmental disorders, even in individuals with known monogenic diagnoses.

The paper shows that PCDH19 mutations in females cause abnormal cell sorting in the developing cortex, leading to epilepsy, while males are unaffected due to normal sorting.