Entries by Martin Danner

Finding the Needle in the Genomic Haystack

Every human genome contains millions of genetic variants. Most are harmless but occasionally a single change can cause a serious disease. Finding that one variant is a needle‑in‑the‑haystack problem and increasingly a challenge tackled with machine learning.

Machine learning workflow for evaluating genetic variants based on protein structure embeddings

Missense variants, that is, single amino acid substitutions in proteins, are often difficult to assess. Our machine learning workflow uses protein structure-based graph embeddings to predict the pathogenicity of such variants. In doing so, the structural information enhances existing approaches like the CADD score and provides new insights for genomic medical diagnostics.