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Project: Computer aided prognosis of chronic liver diseases

Motivation:

Liver fibrosis is a connective tissue like alteration of the liver which raises from the chronic damage of liver tissues. Persistent damage leads from fibrosis to cirrhosis which further develops into serious liver damage and clinical symptoms. Thus, the development of a cirrhosis is involved in severe complications like liver failure or the emergence of liver cancer (hepatocellular carcinoma, HCC). In Germany, it is estimated that about 2.000.000 patients suffer from fatty liver disease and 400.000 patients are infected with chronic Hepatitis B and 600.000 with Hepatitis C. Furthermore with 500.000 alcohol-related liver cirrhosis as well as a significant number of patients with metabolic liver diseases, the emergence of fibrosis and the following cirrhosis is an essential health problem. The development of liver fibrosis results in an imbalance between the regulation of the expression and synthesis of collagens and reduced matrix degradation by collagenases. As a consequences there are chronic wounds and inflammations, necrosis as well as a massive expression of extra cellular matrix (ECM) proteins. If this imbalance remains over a longer time period, an alteration of healthy liver tissue into inoperative scar tissue is conducted. This cirrhotic modification can subsequently lead to HCC and despite the increasing incidence of HCC the treatment options are still insufficient.

Research Project:

  • The main focus of this research project is the implementation of a graph enrichment algorithm to score biological interaction networks for their relevance within pathological/biological states. The implementation basis of this algorithm is the UniPAX framework, a biological data warehouse for pathway based information.
  • Another focus is devoted to the application of the graph enrichment algorithm to identify significant interaction networks with respect to patients suffering from liver fibrosis, liver cirrhosis and liver cancer and the evaluation of the most promising interaction networks for therapeutic targets.
  • Additionally, this project is dedicated to provide a webapplication which deals with individual gene lists, uploaded by users, and output scored interaction networks for those gene lists.