The group aims to develop and apply statistical and bioinformatic methods in genetics. This comprises e.g. the planning and conduction of studies, the management and analysis of high-dimensional molecular genetic data (germ line mutations, tumour cell line mutations, expression data, metabolomics), population genetics and integrative genome analyses. The work is accompanied by statistical or continuous modelling of diseases or physiological processes. For this purpose, we cooperate with different national and international study groups of different disease entities or phenotypes.
Genetical Statistics and Systems-Biology Group
Genetical Statistics and Systems-Biology Group at Leipzig University - Systems-medical Modeling and Multi-omics Analyses
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Showing 10 of 27 repositories
- 2412_scRNA_lung_organoid_chip Public
Code for scRNA seq analysis with 10x data processing human lung data
GenStatLeipzig/2412_scRNA_lung_organoid_chip’s past year of commit activity - Deep-learning-aided-inter-species-comparison-in-cynomolgus-monkey-and-humans Public
Scripts accompanying the manuscript "Deep learning-aided inter-species-comparison reveals shared and distinct molecular patterns in cynomolgus monkey and humans following non-specific T cell activation"
GenStatLeipzig/Deep-learning-aided-inter-species-comparison-in-cynomolgus-monkey-and-humans’s past year of commit activity - 241026_struck_hypothermia Public
Code used to analyse Struck et al. Admission hypothermia in trauma patients undergoing prehospital tracheal intubation: 15-year review of a level-1 trauma center
GenStatLeipzig/241026_struck_hypothermia’s past year of commit activity - Neutrophil-chemoattractant-CXCL5-contributes-to-loss-of-lung-barrier-integrity-in-acute-lung-injury Public
Scripts and data accompanying the manuscript "Neutrophil-chemoattractant CXCL5 contributes to loss of lung barrier integrity in acute lung injury"
GenStatLeipzig/Neutrophil-chemoattractant-CXCL5-contributes-to-loss-of-lung-barrier-integrity-in-acute-lung-injury’s past year of commit activity - Neural-Networks-Assisted-Humanization-of-COVID-19-Hamster-scRNASeq-data Public
Scripts accompanying Publication Friedrich VD & Pennitz P et al. "Neural Network-Assisted Humanization of COVID-19 Hamster scRNASeq data Reveals Matching Severity States in Human Disease" "
GenStatLeipzig/Neural-Networks-Assisted-Humanization-of-COVID-19-Hamster-scRNASeq-data’s past year of commit activity - GwasHelpR Public
GenStatLeipzig/GwasHelpR’s past year of commit activity
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