As a partner MicroDiscovery has worked with a multitude of partners from all over the globe. Since 20 years our Research and Development Team has closely worked with other researchers and developers in a variety of pioneering projects at national and international levels. We provide our partners with expertise in all areas of biomedical analysis, software development and data management.

Excerpt of current projects

Genetic factors influencing the course of a SARS-CoV-2 infection: epidemiologic investigation of peptide signatures, immunity, genetic predisposition, and alterations in SARS-CoV-2

Harmless, serious or even fatal: Which role do genetic factors play in the course of an infection with SARS-CoV-2?

The EPI-Dx study currently being conducted by ATLAS Biolabs, in.vent Diagnostica and MicroDiscovery investigates this question which has so far remained unanswered.

For more information see the press information below.


Excerpt of finished projects

Hepatic and Cardiac Toxicity Systems modelling

The HeCaToS project (Hepatic and Cardiac Toxicity Systems modelling) aims at developing integrative in silico tools for predicting human liver and heart toxicity. The objective is to develop an integrated modeling framework, by combining advances in computational chemistry and systems toxicology, for modelling toxic perturbations in liver and heart across multiple scales.

MicroDiscovery was responsible for the development of new modelling algorithms and provided assistance in the statistical analysis of Next Generation Sequencing and proteomics data.




Systems medicine approach to personalized immunosuppressive treatment at early stage after Kidney Transplantation.

The e:KID project generates a spectrum of different data types including clinical data, gene expression data, cytokine data, epigenetics data, metabolomics data as well as data derived from HLA antibody measurements and viral load measurements. We create and manage an integrative database resource for all data generated in the consortium. In addition, MicroDiscovery is performing data analysis tasks, like machine learning and gene enrichment analysis.


Machine Learning with Relational Background Knowledge for Biomedical Applications

Machine learning approaches have emerged as the state-of-the-art methodology to infer predictions from large-scale data with numerous applications in science and economy. The research project aims to develop novel machine learning methods and apply these to the prediction of cancer therapy success in precision medicine. The goal of the project is to learn the sensitivity of the drug response of a biological system (cell line, patient tumors) from its molecular features and their relationships. Novel methods will develop constraints that allow better inclusion of background knowledge on biological pathways and gene-gene relationships.

MicroDiscovery is generating a new knowledge base including information obtained via text mining. We apply modern text mining alogrithm on all available data sets to enhance the predicitve models of our partners.


Capillary driven Platform for Multiplex Protein Analytics

The cryoPOC consortium is developing a new microfluidic platform to perform immunochromatographic assays. We are aiming at a capillary driven platform for multiplex protein analytics, with an innovative concept based on an optically transparent capillary and porous polymer materials, serving as the microfluidic carrier. The capillary will include a series of separate segments with different capture molecules and controls; together with a handheld device for fluorescence detection this platform will enable two-color detection and analysis of multiplex immunoassays directly at the point of need.

MicroDiscovery is responsible for the statistical analysis throughout the projects and develops the platform and new software designed at evalutating the capillaries.




P. Prasse, P. Iversen, M. Lienhard, K. Thedinga, C. Bauer, R. Herwig, and T. Scheffer. Matching anticancer compounds and tumor cell lines by neural networks with ranking loss. NAR Genom Bioinform, 4, pp. lqab128, 2022. doi: 10.1093/nargab/lqab128.

A. Blazquez-Navarro, C. Bauer, N. Wittenbrink, K. Wolk, R. Sabat, C. Dang-Heine, S. Neumann, T. Roch, P. Wehler, R. Blazquez-Navarro, S. Olek, O. Thomusch, H. Seitz, P. Reinke, C. Hugo, B. Sawitzki, N. Babel, and M. Or-Guil. Early prediction of renal graft function: Analysis of a multi-center, multi-level data set. Curr Res Transl Med, 70, pp. 103334, 2022. doi: 10.1101/2021.01.04.20248473.


C. Bauer, R. Herwig, M. Lienhard, P. Prasse, T. Scheffer, and J. Schuchhardt. Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types. J Transl Med, 19, pp. 274, 2021. doi: 10.1186/s12967-021-02941-z

P. L. Fosso Tene, A. Stumpf, M. Zinggeler, V. Reuck, A. Malik, W. Weigel, M. Müller, R. Kneusel, T. Brandstetter, and J. Rühe. Linear cryogel arrays: on the fast track for Borreliosis detection. Anal. Chem. 2021, 93, 36, 12426–12433. doi: 10.1021/acs.analchem.1c02561.


N. Selevsek, F. Caiment, R. Nudischer, H. Gmuender, I. Agarkova, F. L. Atkinson, I. Bachmann, V. Baier, G. Barel, C. Bauer, S. Boerno, N. Bosc, O. Clayton, H. Cordes, S. Deeb, S. Gotta, P. Guye, A. Hersey, F. M. I. Hunter, L. Kunz, A. Lewalle, M. Lienhard, J. Merken, J. Minguet, B. Oliveira, C. Pluess, U. Sarkans, Y. Schrooders, J. Schuchhardt, I. Smit, C. Thiel, B. Timmermann, M. Verheijen, T. Wittenberger, W. Wolski, A. Zerck, S. Heymans, L. Kuepfer, A. Roth, R. Schlapbach, S. Niederer, R. Herwig, and J. Kleinjans. Network integration and modelling of dynamic drug responses at multi-omics levels. Commun Biol, 3, pp. 573, 2020. doi: 10.1038/s42003-020-01302-8.