Miriam Elbracht

The paper systematically perturbs the human genome to reveal a relational network of genomic dependencies, uncovering 'relons' that reflect functional and evolutionary constraints.

The paper identifies RNU2-2 variants as a frequent cause of neurodevelopmental disorders, both dominant and recessive, in a large cohort of individuals with rare disorders.

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

Genolator is a multimodal large language model that combines genomic and structural data with natural language to interpret protein function, outperforming existing models in accuracy.

This paper predicts the pathogenicity of missense variants using graph embeddings of protein structures generated by ESMFold and XGBoost.

The paper analyzes mutational constraint in short open reading frames (sORFs) and their genomic neighbors using the gnomAD 4.0 dataset, identifying a subset of highly constrained sORFs similar to canonical genes.

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

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 identifies mutations in the C2orf69 gene as the cause of a human multisystem disorder characterized by autoinflammation, leukoencephalopathy, and hepatopathy.

This study identifies a DEGS1 mutation causing sphingolipid metabolism defects, leading to neurological disorders and suggests dietary intervention as a potential treatment.