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Functional evaluation of domain-domain interactions and human protein interaction networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45392

Schlicker,  Andreas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44662

Huthmacher,  Carola
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

Ramírez,  Fidel
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons43993

Albrecht,  Mario
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44815

Kohlbacher,  Oliver
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Zitation

Schlicker, A., Huthmacher, C., Ramírez, F., Lengauer, T., & Albrecht, M. (2006). Functional evaluation of domain-domain interactions and human protein interaction networks. In German Conference on Bioinformatics (GCB 2006) (pp. 115-126). Bonn, Germany: Gesellschaft für Informatik.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-22F2-9
Zusammenfassung
Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple Gene Ontology terms. Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence.