Data Analysis


Background: Methods for integrative Analysis of Omics Data

The development of high-throughput molecular technologies has provided a large amount of data in biomedical research and applications. In particular, data from sequencing or mass spectroscopy, but also image data and clinical data play an important role. These data describe the states of complex cellular systems and can also be linked to the progression and prognosis of complex diseases. New analytical procedures and methods for bioinformatic evaluation, which allow the interpretation of such data, urgently need to be further developed. Such method development requires intensive research work.

Head of the group: Prof. Beißbarth

In this context, methods from the field of machine learning and careful statistical evaluation of these methods are often required.
Ultimately, it is extremely important to present the results of such a complex analysis in a way that is understandable to the biomedical researcher. Therefore we need tools and methods to evalate and visualize such results.

Research Foci

The focus of this group is the development of such tools and methods. We apply graph-based and network reconstruction methods to create models of signaling pathways based on Omics data. We also developed methods to use prior biological knowledge e.g. from gene ontology, protein interactions, transcription factor binding sites and protein domain structure in the interpretation of biomedical data. Pathway analysis and gene set enrichment methods can help in the biomedical interpretation of results from high-throughput experiments.

Current projects

PerMiCCion (2022 - 2026)
Performing a comprehensive characterization of the tissue micro- and mycobiome of young CRC patients, identifying the oncogenic microbiome signature and understanding its influence on oncogenic signaling resulting in tumor development and progression. To achieve our scientific goals, we have formed a strategic research alliance in PerMiCCion of world-leading scientists with complementary background: Colorectal cancer, cancer and nutritional epidemiology, molecular oncology, metagenomics and medical informatics. For more information see the project page as well as the officiel website

FAIrPaCT (2022 - 2024)
The aim of the project is to develop FAIrPaCT, a software system supported by federated artificial intelligence. For more information see the project site or the BMBF project-site.

MTB-Report (2020 - 2024)
In this project, we aim to develop a tool that suggest therapies for tumor patients based on thier biomarkers. For more details, please see the project page.

CRU 5002 (Pancreatic carcinoma) (2020 - 2024)
Pancreatic ductal adenocarcinoma (PDAC) is considered one of the greatest clinical challenges of modern cancer medicine. Our department is part of the Clinical Research Group "Characterisation and Targeting of Genome Dynamics for a Subtype-Specific Therapy of Pancreatic Carcinoma" . The aim of this clinical research group is to analyse further subtypes by studying the genome dynamics of the carcinoma and thus contribute to the development of individualised therapies. For more information please see the project page.

MATCH (2019 - 2023)
The aim of MATCH is the identification of a biomarker signature which associates with beneficial outcome on specific lipid therapy, and ideally is not dynamic under ongoing therapy. For more information, see the project site.

MyPathSem (2016 - 2021)
In the MyPathSem project we aim in a collaboration with other Department of the UMG, to extract the patient sprecific parts of the molecular networks. For more information see the project site.

Selected Publications

Chereda H, Bleckmann A, Menck K, Perera-Bel J, Stegmaier P, Auer F, Kramer F, Leha A, Beißbarth T.:
Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
Genome Med. 2021 Mar 11;13(1):42.
doi: 10.1186/s13073-021-00845-7

Perera-Bel J., Hutter B., Heining C., Bleckmann A., Fröhlich M., Fröhlich S., Glimm H., Brors B., Beißbarth T.:
From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards.
Genome Med. 10(1):18
doi: 10.1186/s13073-018-0529-2

Wolff A., Perera-bel J., Schildhaus HU, Homayounfar K., Schatlo B., Belckmann A., Beißbarth T.:
Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations.
Stud. Health Technol. Inform. 253:217-221

Chereda H., Bleckmann A., Kramer F., Leha A., Beisbarth T.:
Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer.
Stud. health Technol. Inform. 2019 Sep 3;267:181-186

Sitte M., Menck K., Wachter A., Reinz E., Korf U., Wiemann S., Bleckmann A., Beissbarth T.:
Reconstruction of Different Modes of WNT Dependant Protein Networks from Time Series Protein Quantification.
Stud. health Technol. Inform. 2019 Sep 3;267:175-180

Wachter A., Beißbarth T.:
Decoding cellular dynamics in epidermal growth factor signaling using a new pathway-driven integration approach for proteomics and transcriptomics data.
Frontiers in genetics, 2015,6:351

Members of the group

    Currently the following people work together to achieve our aims:

    Former members of the group

    • Darius Wlochowitz 
    • Ryan Daou
    • Manasa Kalya Purushothama
    • Maren Sitte
    • Alexander Wolff
    • Florian Auer
    • Julia Perrera
    • Michaela Bayerlova
    • Astrid Wachter
    • Frank Kramer
    • Silvia von der Heyde

    Bachelor- or Master thesis

      We offer different topics for Bachelor- or Master thesis in this field.
      If you are interessed, please contact:

      Project Manager

      Dr. Daniela Großmann

      Dr. Daniela Großmann

      contact information

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