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


The goal of the project is to develop new methods for assessing drug toxicity in the liver and heart based on network modeling and dynamic, dose-dependent data from heterogeneous omics experiments and publicly available literature data. Within the project, MicroDisocvery will implement the following sub-aspects:

  • Evaluation of literature data.
  • Analysis of public datasets and project-internal datasets.
  • Establishment of a knowledge base for managing the project results.

Expert system to support therapy decision-making in renal cell carcinoma. (ESTHER)

The ESTHER projects aims to develop and validate a clinic-compatible worklfow for the identification and assessment of therapeutical procedures of renal cell carcinoma. The workflow consists of automated sample preparation of tumor tissue, LC-MS/MS measurement routine, data analysis, and a reporting system for proteomics.

MicroDiscovery will be responsible for data analysis and the implementation of necessary reporting mechanism throughout the project.


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.


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.




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.

Gloria S Benson, Chris Bauer, Lucrezia Hausner, Samuel Couturier, Piotr Lewczuk, Oliver Peters, Michael Hüll, Holger Jahn, Frank Jessen, Johannes Pantel, Stefan J Teipel, Michael Wagner, Johannes Schuchhardt, Jens Wiltfang, Johannes Kornhuber, and Lutz Frölich. Don't forget about tau: the effects of ApoE4 genotype on Alzheimer's disease cerebrospinal fluid biomarkers in subjects with mild cognitive impairment-data from the Dementia Competence Network. J. Neural Transm. (Vienna), 2022. doi: 10.1007/s00702-022-02461-0

Verheijen M, Sarkans U, Wolski W, Jennen D, Caiment F, Kleinjans J; HeCaToS Consortium. Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds. Sci Data. 2022 Nov 14;9(1):699. doi: 10.1038/s41597-022-01825-1. PMID: 36376331; PMCID: PMC9663581.


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.

A. Blazquez-Navarro21, C. Dang-Heine, P. Wehler, T. Roch, C. Bauer, S. Neumann, R. Blazquez-Navarro, A. Kurchenko, K. Wolk, R. Sabat, T. H. Westhoff, S. Olek, O. Thomusch, H. Seitz, P. Reinke, C. Hugo, B. Sawitzki, M. Or-Guil, and N. Babel. Risk factors for Epstein-Barr virus reactivation after renal transplantation: Results of a large, multi-centre study. Transpl Int, 34, pp. 1680–1688, 2021. doi: 10.1111/tri.13982


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.

M. Fuentes, A. Klostermann, L. Kleineidam, C. Bauer, J. Schuchhardt, W. Maier, F. Jessen, L. Frölich, J. Wiltfang, J. Kornhuber, S. Klöppel, V. Schieting, S. J. Teipel, M. Wagner, and O. Peters. Identification of a Cascade of Changes in Activities of Daily Living Preceding Short-Term Clinical Deterioration in Mild Alzheimer's Disease Dementia via Lead-Lag Analysis. J Alzheimers Dis, 76, pp. 1005–1015, 2020. doi: 10.3233/JAD-20023010.3233/JAD-200230

F. Menne, C. G. Schipke, A. Klostermann, M. Fuentes-Casañ, S. D. Freiesleben, C. Bauer, and O. Peters. Value of Neuropsychological Tests to Identify Patients with Depressive Symptoms on the Alzheimer's Disease Continuum. J Alzheimers Dis, 78, pp. 819–826, 2020. doi: 10.3233/JAD-20071010.3233/JAD-200710


J. R. F. Santos, C. Bauer, J. Schuchhardt, D. Wedekind, K. Waniek, I. Lachmann, J. Wiltfang, and J. Vogelgsang. Validation of a prototype tau Thr231 phosphorylation CSF ELISA as a potential biomarker for Alzheimer's disease. J Neural Transm (Vienna), 126, pp. 339–348, 2019. doi: 10.1007/s00702-019-01982-5

N. Wittenbrink, S. Herrmann, A. Blazquez-Navarro, C. Bauer, E. Lindberg, K. Wolk, R. Sabat, P. Reinke, B. Sawitzki, O. Thomusch, C. Hugo, N. Babel, H. Seitz, and M. Or-Guil. A novel approach reveals that HLA class 1 single antigen beadsignatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection in renal transplantation. BMC Immunol., 20, pp. 11, 2019. doi: 10.1186/s12865-019-0291-210.1186/s12865-019-0291-2


I. Virant-Klun, C. Bauer, A. Stahlberg, M. Kubista, and T. Skutella. Human oocyte maturation in vitro is improved by coculture with cumulus cells from mature oocytes. Reprod. Biomed. Online, 2018. doi: 10.1016/j.rbmo.2018.01.011

L. K. Joachim ; L. Frölich ; E. Rüther ; J. Wiltfang ; W. Maier ; J. Kornhuber ; C. Bauer ; I. Heuser ; O. Peters. Correlation of CSF- and MRI-Biomarkers and Progression of Cognitive Decline in an Open Label MCI Trial. The Journal of Prevention of Alzheimer’s Disease (JPAD), 5, 2018. doi: 10.14283/jpad.2018.5

H. Shahpasand-Kroner, H. W. Klafki, C. Bauer, J. Schuchhardt, M. Huttenrauch, M. Stazi, C. Bouter, O. Wirths, J. Vogelgsang, and J. Wiltfang. A two-step immunoassay for the simultaneous assessment of Aβ38, Aβ40 and Aβ42 in human blood plasma supports the Aβ42/Aβ40 ratio as a promising biomarker candidate of Alzheimer's disease. Alzheimers Res Ther, 10, pp. 121, 2018. doi: 10.1186/s13195-018-0448-x

C. Hardt, C. Bauer, J. Schuchhardt, and R. Herwig. Computational Network Analysis for Drug Toxicity Prediction. Methods Mol. Biol., 1819, pp. 335–355, 2018. doi: 10.1007/978-1-4939-8618-7_16

A. Blazquez-Navarro, C. Dang-Heine, N. Wittenbrink, C. Bauer, K. Wolk, R. Sabat, T. H. Westhoff, B. Sawitzki, P. Reinke, O. Thomusch, C. Hugo, M. Or-Guil, and N. Babel. BKV, CMV, and EBV Interactions and their Effect on Graft Function One Year Post-Renal Transplantation: Results from a Large Multi-Centre Study. EBioMedicine, 34, pp. 113–121, 2018. doi: 10.1016/j.ebiom.2018.07.017


L. Frolich, O. Peters, P. Lewczuk, O. Gruber, S. J. Teipel, H. J. Gertz, H. Jahn, F. Jessen, A. Kurz, C. Luckhaus, M. Hull, J. Pantel, F. M. Reischies, J. Schroder, M. Wagner, O. Rienhoff, S. Wolf, C. Bauer, J. Schuchhardt, I. Heuser, E. Ruther, F. Henn, W. Maier, J. Wiltfang, and J. Kornhuber. Incremental value of biomarker combinations to predict progression of mild
cognitive impairment to Alzheimer's dementia. Alzheimers Res Ther, 9, pp. 84, 2017. doi: 10.1186/s13195-017-0301-7


H. W. Klafki, H. Hafermann, C. Bauer, U. Haussmann, I. Kraus, J. Schuchhardt, S. Muck, N. Scherbaum, and J. Wiltfang. Validation of a Commercial Chemiluminescence Immunoassay for the Simultaneous Measurement of Three Different Amyloid-β Peptides in Human Cerebrospinal Fluid and Application to a Clinical Cohort. J. Alzheimers Dis., 54, pp. 691–705, 2016. doi: 10.3233/JAD-160398

Gabriel CH, Gross F, Karl M, Stephanowitz H, Hennig AF, Weber M, Gryzik S, Bachmann I, Hecklau K, Wienands J, Schuchhardt J, Herzel H, Radbruch A, Krause E, Baumgrass R. Identification of Novel Nuclear Factor of Activated T Cell (NFAT)-associated Proteins in T Cells. J Biol Chem. 2016 Nov 11;291(46):24172-24187. doi: 10.1074/jbc.M116.739326. Epub 2016 Sep 16. PMID: 27637333; PMCID: PMC5104941.


C. Bauer, K. Stec, A. Glintschert, K. Gruden, C. Schichor, M. Or-Guil, J. Selbig, and J. Schuchhardt. BioMiner: Paving the Way for Personalized Medicine . Cancer Inform, 14, pp. 55–63, 2015. doi: 10.4137/CIN.S20910

Fang Z, Hecklau K, Gross F, Bachmann I, Venzke M, Karl M, Schuchhardt J, Radbruch A, Herzel H, Baumgrass R. Transcription factor co-occupied regions in the murine genome constitute T-helper-cell subtype-specific enhancers. Eur J Immunol. 2015 Nov;45(11):3150-7. doi: 10.1002/eji.201545713. Epub 2015 Sep 21. PMID: 26300430.


C. Bauer, A. Glintschert, and J. Schuchhardt. ProfileDB: a resource for proteomics and cross-omics biomarker discovery . Biochim. Biophys. Acta, 1844, pp. 960–966, 2014. doi: 10.1016/j.bbapap.2013.11.007.


U. Haussmann, O. Jahn, P. Linning, C. Janssen, T. Liepold, E. Portelius, H. Zetterberg, C. Bauer, J. Schuchhardt, H. J. Knolker, H. Klafki, and J. Wiltfang. Analysis of amino-terminal variants of amyloid-beta peptides by capillary isoelectric focusing immunoassay. Anal. Chem., 85, pp. 8142–8149, 2013. doi: 10.1021/ac401055y.


C. Bauer, F. Kleinjung, D. Ruthishauser, C. Panse, A. Chadt, T. Dreja, H. Al-Hasani, K. Reinert, R. Schlapbach, and J. Schuchhardt. PPINGUIN: Peptide Profiling Guided Identification of Proteins Improves Quantitation of iTRAQ Ratios. BMC Bioinformatics, 13, pp. 34, 2012. doi: 10.1186/1471-2105-13-34.

K. Gruden, M. Hren, A. Herman, A. Blejec, T. Albrecht, J. Selbig, C. Bauer, J. Schuchardt, M. Or-Guil, K. Zupancic, U. Svajger, B. Stabuc, A. Ihan, A. N. Kopitar, M. Ravnikar, M. Knezevic, P. Rozman, and M. Jeras. A 'crossomics' study analysing variability of different components in peripheral blood of healthy caucasoid individuals. PLoS ONE, 7, pp. e28761, 2012. doi: 10.1371/journal.pone.0028761.


C. Bauer, F. Kleinjung, C. J. Smith, M. W. Towers, A. Tiss, A. Chadt, T. Dreja, D. Beule, H. Al-Hasani, K. Reinert, J. Schuchhardt, and R. Cramer. Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset. BMC Bioinformatics, 12, pp. 140, 2011.

C. Bauer, R. Cramer, and J. Schuchhardt. Evaluation of peak-picking algorithms for protein mass spectrometry. Methods Mol. Biol., 696, pp. 341–352, 2011. doi: 10.1007/978-1-60761-987-1_22