LiSyM: The better way to manage your data
Free and open platform for easier research data management
Latest additions
MSPypeline: a python package for streamlined data analysis of mass spectrometry-based proteomics.
Publication - added 10 days agoGuided interactive image segmentation using machine learning and color-based image set clustering.
Publication - added 10 days agoOptimization of extracellular matrix for primary human hepatocyte cultures using mixed collagen-Matrigel matrices.
Publication - added 20 days agoHepatocyte apical bulkheads provide a mechanical means to oppose bile pressure
Publication - added about 1 month agoCapsaicin receptor TRPV1 maintains quiescence of hepatic stellate cells in the liver via recruitment of SARM1
Publication - added about 2 months agoSerum Glial Cell Line-Derived Neurotrophic Factor (sGDNF) Is a Novel Biomarker in Predicting Cirrhosis in Patients with Chronic Hepatitis B
Publication - added about 2 months agoFOXA2 prevents hyperbilirubinaemia in acute liver failure by maintaining apical MRP2 expression
Publication - added about 2 months agoA hierarchical regulatory network ensures stable albumin transcription under various pathophysiological conditions
Publication - added about 2 months agoTransforming growth factor β latency: A mechanism of cytokine storage and signalling regulation in liver homeostasis and disease
Publication - added about 2 months agoFollistatin‐controlled activin‐HNF4α‐coagulation factor axis in liver progenitor cells determines outcome of acute liver failure
Publication - added about 2 months ago
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LiSyM (Liver Systems Medicine) represents a research network of German centers and institutions, brought together by a 20 Million Euro funding program of the German Government, in which mathematicians, modelers, pharmacologists, molecular biologists and clinical scientists work together to develop a Systems Medicine approach to study early and advanced liver disease.
The aim of this unique research program is to acquire and use new experimental data and data from existing data bases to build computational models that facilitate decision making at the patient's bedsite and to predict the actions of new medicines in the treatment of metabolic liver disease.