Extracting subtypes of illness from Molecular data
Technical University of Munich (Germany), May 27, 2022
Scientists at Germany's Technical University of Munich (TUM) have developed a machine learning algorithm to extract subtypes of illnesses from molecular data. The Molecular Signatures using Biclustering (MoSBi) tool merges the results of existing algorithms to acquire stronger, more precise clinical subtype predictions, removing the need for time-consuming adjustment. "We have developed a Web-based tool that permits online analysis of molecular clinical data by practitioners without prior knowledge of bioinformatics," explained TUM's Josch Konstantin Pauling. Researchers can submit data to a website for automated analysis, and use the results to interpret their research. The team worked with colleagues at Germany’s Max Planck Institute, Technical University of Dresden, and Kiel University Clinic to apply MoSBI to identify two potential biomarkers for progression to non-alcoholic fatty liver disease....'
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