A large amount of data from a variety of experiments is available on physical or genetical interactions between proteins. We are interested in how these data can be used to predict the functions proteins perform in the organism. Even without knowing details about how the proteins interact, there is statistical evidence that interacting proteins have an increased probability to be functionally related. Based on probabilistic models such a Markov random fields, we try to find statistical parameters to identify possible candidate for new functional assignments.

The result of this project is summarized in the following paper:

• C. Best, R. Zimmer, J. Apostolakis:
  Probabilistic methods for predicting protein functions in protein-protein interaction networks
  in: German Conference on Bioinformatics 2004 (to be published), October 4-6, 2004..
  [PDF]  ·  [Presentation - PDF]