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Network meta-analysis for indirect inference from published omics-studies

Methods:

Extraction of knowledge from published studies and data; avoidance of animal experiments by indirect inference from existing results; merging of study results using data science techniques; methods for simulation of high-dimensional data structures


Project description:

Network meta-analysis can be used to perform indirect group comparisons from existing study results, i.e. group comparisons that have not been studied in the original studies. Thus, animal experiments can be avoided. In this project, the way of using network meta-analysis on high-dimensional data from omics experiments (e.g. transcriptome, proteome or metabolome profiles) is studied. Furthermore, methods for extracting new knowledge from public, experimental omics data by means of pattern recognition are studied.

Besides, methods to simulate high-dimensional data are developed. These techniques can contribute to perform computer simulations of transcriptome, proteome and metabolome studies.

 

Project Lead:

Prof. Dr. Klaus Jung (klaus.jung@tiho-hannover.de; 0511-953-8878)

Institute for Animal Breeding and Genetics, Working Group Genomics and Bioinformatics of Infectious Diseases, University of Veterinary Medicine Hannover, Foundation, Germany

 

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