Astrid Dempfle

The paper evaluates multi-laboratory studies to identify promising treatments by assessing effect sizes and experimental rigor across different medical fields.

The paper uses statistical learning to decode T cell receptor associations with HLA alleles using a large dataset, identifying clonotypes linked to specific HLA alleles and infections.

This study identifies HLA-associated CDR3-QTLs that influence T-cell receptor composition in IBD and healthy controls, revealing disease-specific and universal effects on TRB repertoires.

The paper identifies novel CNV loci and dose-dependent gene effects across six psychiatric disorders using genome-wide analysis.

The paper identifies shared and divergent genetic mechanisms across psychiatric disorders using gene-set burden analysis of copy number variants.

The paper uses resting-state EEG and machine learning to stratify responses to tDCS in healthy children, identifying electrophysiological profiles linked to positive or negative responses.

This study investigates genetic mechanisms underlying sex bias in ADHD prevalence using genome-wide data and finds no mean polygenic burden difference across sexes but increased risk in female siblings and comorbid conditions.