Rate My Professor Michael Menden

Menden's research develops advanced biostatistical and machine learning methods applied to deep molecularly characterized biomedical datasets to uncover disease etiologies and identify intervention strategies, with a strong emphasis on precision medicine and drug discovery. His expertise includes computational cancer pharmacogenomics, analyzing high-throughput drug screens, CRISPR lethality, and resistance data to predict drug sensitivity, synergy, and biomarkers for patient stratification. This extends to translational pharmacogenomics in tuberculosis resistance, inflammatory skin diseases, neurodegenerative disorders, and diabetes through projects like bayresq.net and UNITE4TB. He has published 33 peer-reviewed papers amassing over 5,200 citations and an h-index of 20. Notable works include 'A landscape of pharmacogenomic interactions in cancer' (Cell, 2016), 'Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties' (PLoS One, 2013), 'Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen' (Nature Communications, 2019), and 'Generative artificial intelligence empowers digital twins in drug discovery and clinical trials' (Expert Opinion on Drug Discovery, 2024). Menden received the European Research Council Starting Grant in 2020 and the Rising Star in Drug Discovery Award from the University of Cardiff in 2022. He co-filed a patent on generative AI for clinical trials with Roche in July 2023 and collaborates with leading institutions such as the Sanger Institute, AstraZeneca, and Technical University of Munich.