Marcel Kucharík

The paper combines expert guideline-based and machine learning approaches to improve automated prediction of the clinical effect of copy number variations.

The paper presents a classifier to predict the clinical impact of structural copy number variations with over 98% accuracy.

The paper introduces Varlock, a method to securely store genomic data by masking personal alleles using population frequencies, ensuring privacy while preserving useful data for research.

This study evaluates the detection limits of chromosomal microdeletions in non-invasive prenatal testing using whole genome sequencing data and simulated datasets.

The paper introduces a method to reduce uninformative calls in NIPT by analyzing the length profile of circulating cell-free DNA, improving resolution of ambiguous results when combined with z-scores.