Menden Lab: Leveraging Biostatistics and AI/ML in Drug Discovery and Development
The mission of our research group is to develop biostatistical and machine learning frameworks applied to biomedical data, to retrieve insights in the aetiology of complex diseases and identify novel intervention strategies. For this, we explore deep molecular characterised biomedical datasets, environmental factors, and tailor our models depending on disease specific knowledge gained through collaborations, literature and data driven analyses, thus empowering the next generation of drug target identification, drug repositioning and precision medicine.
Computational Cancer Pharmacogenomics
We have strong expertise is in computational method development for cancer pharmacogenomics including the analysis of monotherapy and drug combination high-throughput screens, and CRISPR lethality and drug resistance screens, which is highlighted by our ERC project and publication record. The Menden Lab customises machine learning and biostatistical methods to predict drug sensitivity and synergy, as well as derived genetic biomarkers of these responses. Our work focuses on clinical translatability for enabling patient stratification based on deep molecular profiles, which is the key pillar of precision.
Translational Computational Pharmacogenomics
The foundation of our endeavour is our strong expertise in Computational Cancer Pharmacogenomics (ERC StG), which we envision to generalise to the Translational Computational Pharmacogenomics. In particular, we expand our research focus to tuberculosis drug resistance (bayresq.net, UNITE4TB), inflammatory skin diseases(IGSSE), neurodegenerative diseases (JPND, MUDS) and diabetes research (DZD). Common across all projects, we are experts in analysing biological models which are deep molecularly and phenotypic characterised, which ultimately enables precision medicine. In essence, our research vision is to establish a Translational Computational Pharmacogenomics programme in cancer and beyond, in order to accelerate the delivery of urgently needed targeted therapies.
Read more about Michael and his research here.
Dr Michael Menden, Head of Laboratory
- Sanger Institute, UK, Dr. M. Garnett, GDSC / OpenTargets / COSMIC / DepMap
- AstraZeneca, UK, Dr. Ultan McDermott, pooled CRISPR screens
- University of Uppsala, Sweden, Dr. Y. Mao, CRISPR and Immuno Oncology HTS
- TUM, Germany, Prof. Dr. D. Krappmann, pharmacogenomics
- TUM, Germany, Prof. Dr. D. Sauer, GEMM derived PAAD cell culture HTS
- TUM, Germany, Prof. Dr. M. Gerhard, STAD cancer organoid HTS and helicobacter resistance
- TUM, Germany, Prof. Dr. P. Lingor, Parkinson’s disease and ALS
- ZAUM, Germany, Dr. S. Eyerich, Molecular biology and single cell sequencing
- LMU, Germany, Dr. A. Wieser, Infections molecular biology
- LMU, Germany, Prof. Dr. M. Hölscher, Tuberculosis
- LMU, Germany, Prof. Dr. V. Heinemann, Colorectal cancer clinical trials
- Karolinska, Sweden, Prof. Dr. K. Eyerich, Inflammatory skin diseases
- DZD, German Diabetes Center, Germany, Prof. Dr. C. Herder, Type 2 diabetes
- DZD, German Diabetes Center, Germany, Prof. Dr. M. Roden, Type 2 diabetes
- DZD, German Diabetes Center, Germany, Prof. Dr. A. Schürmann, Type 2 diabetes
- DZD, German Diabetes Center, Germany, Prof. Dr. R. Holl, Type 1/2 diabetes
- Helmholtz Munich, Germany, Dr. M. Heinig, Genomics analysis
- Helmholtz Munich, Germany, Dr. P. Casale, Functional genomics analysis
- Helmholtz Munich, Germany, Dr. B. Schubert, Immuno oncology analysis
- Helmholtz Munich, Germany, Prof. Dr. Dr. F. Theis, Single cell sequencing
- Helmholtz Munich, Germany, Prof. Dr. C. Müller, Microbiome analysis
- Helmholtz Munich, Germany, Dr. A. Marsico, Small non-coding RNA analysis
The Laboratory receives funding from the following bodies:
- 2022 JPND, A premotor disease signature for ALS (premodiALS)
- 2022 Bayresq.net Grant, Helicobacter drug resistance prediction (HelcoPredict)
- 2021 DFG-NGS Grant, Pancreatic Cancer Connectivity Map
- 2021 IMI Call UNITE4TB, Identifying Potent Drug Combinations in Tuberculosis (UNITE4TB)
- 2020 ERC Starting Grant, Predicting Potent Drug Combinations by Exploiting Monotherapy Resistance (COMBAT-RES)
- 2020 TUM-IGSSE, Single-cell analysis enhanced molecular diagnostics (SCAMDI)
- 2019 Bayresq.net Grant, Tuberculosis -AI Research (DynamicKit)
- 2019 Independent Junior Group at Helmholtz Zentrum Munich
This research project is available to PhD students, Masters by Research, Honours students, Master of Biomedical Science, Post Doctor Researchers to join as part of their thesis.
Please contact the Research Group Leader to discuss your options.
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For further information about this research, please contact Head of Laboratory Michael P. Menden
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