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.
With our ‘Data Science for Everyday Work’ training course, we aim to help companies bridge the gap in practical knowledge when implementing machine learning projects and foster a shared understanding within the team.
We also offer individual modules that can be combined as desired, and we provide consultancy services for your specific data challenges.
At this year’s Minds Mastering Machines (M3) conference in Karlsruhe, the focus was on best practices for GenAI, RAG systems, case studies from different industries, agent systems, and LLM, as well as legal aspects of ML. We gave three talks about our projects.