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 intesive research work.
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.
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.
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.
CRU 5002 (Pancreatic carcinoma)
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.
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
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
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