Beschreibung:
<jats:title>Significance</jats:title><jats:p>Molecular patient stratification and disease subtyping are ongoing and high-impact problems that rely on the identification of characteristic molecular signatures. Current computational methods show high sensitivity to custom parameterization, which leads to inconsistent performance on different molecular data. Our new method, MoSBi (molecular signature identification using biclustering), 1) enables so far unmatched high performance for stratification and subtyping across datasets of various different biomolecules, 2) provides a scalable solution for visualizing the results and their correspondence to clinical factors, and 3) has immediate practical relevance through its automatic workflow where individual selection, parameterization, screening, and visualization of biclustering algorithms is not required. MoSBi is a major step forward with a high impact for clinical and wet-lab researchers.</jats:p>