Source: HMGU

Dr. Michael Menden
Junior Group Leader

Phone: +49 89 3187-43497
Building/Room: 58a / 003

ResearchGate Profile
Google Scholar Profile


Dr. Michael P. Menden is a Junior Group Leader at the Institute of Computational Biology (Helmholtz Zentrum München) since 2019, and is responsible for the Computational Biomedicine Group. Previously, he worked as Senior Scientist in Oncology Bioinformatics, AstraZeneca, UK. He was a PhD student and postdoctoral fellow at EMBL-EBI, UK. His PhD was awarded in Computational Biology by the University of Cambridge, UK in 2016. In 2017, Dr Menden was appointed an Honorary Lecturer position at the University of Sheffield, UK. Dr Menden is a specialist in the analysis of cancer cell pharmacogenomics high-throughput screens including monotherapy, drug combinations and lately, CRISPR lethality and drug resistance screens. He developed machine learning and statistical methods to predict drug sensitivity and synergy, as well as derived genetic biomarkers of these responses. This work enables patient stratification based on molecular profiles, which is the key pillar of precision medicine.


Selected Publications:

Menden MP*
, Wang D*, Guan Y*, Mason M*, Szalai B*, Bulusu KC*, et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications 2019 (accepted)

Menden MP*, Casale FP*, et al. The germline genetic component of drug sensitivity in cancer cell lines. Nature Communications 2018

Cokelaer T, Chen E, Iorio F, Menden MP, Lightfoot H, Saez-Rodriguez J, Garnett MJ. GDSCTools for Mining Pharmacogenomic Interactions in Cancer. Bioinformatics – Oxford Academics 2017.

Iorio F*, Knijnenburg TA*, Vis DJ*, Bignell GR*, Menden MP*, et al. A landscape of pharmacogenomic interactions in cancer. Cell 2016 

Stransky N*, Ghandi M*, Lehár J*, Amzallag A*, Menden MP*, Iorio F*, et al. (i.e. Cancer Cell Line Encyclopedia Consortium; Genomics of Drug Sensitivity in Cancer Consortium). Pharmacogenomic agreement between two cancer cell line data sets. Nature 2015

* equal contribution


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