Advances in Human Protein Interactome Inference

  • Enrico Capobianco
  • Elisabetta Marras
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Important cellular functions information can be obtained from decomposing Protein-Protein Interaction Networks (PPIN) into constituent groups (complexes, functional modules). Starting from well-covered model organisms (Yeast), our current efiorts are shifting to a complex target organism (Homo Sapiens). It is through statistical techniques and machine learning algorithms that one can proceed with probabilistic steps: assigning unlabelled proteins (classification), inferring unknown functions (generalization), weighting interactions (scoring).


Functional Module Independent Component Analysis Latent Variable Model Homo Sapiens Unlabelled Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    small Bader et al.: Gaining Confidence in High-throughput protein interaction net-works. Nature Biotechnology. 22, 78-85 (2003).CrossRefMathSciNetGoogle Scholar
  2. [2]
    Uetz et al.: A comprehensive analysis of protein-protein interaction in Saccharomyces cerevisiae. Nature. 403, 623-627 (2000).CrossRefGoogle Scholar
  3. [3]
    Gavin et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature. 440, 631-636 (2006).CrossRefGoogle Scholar
  4. [4]
    Krogan et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature. 440, 637-643 (2006).CrossRefGoogle Scholar
  5. [5]
    VonMering et al.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 417, 399-401 (2002).CrossRefGoogle Scholar
  6. [6]
    R. Jansen et al.: Bridging structural biology and genomics: assessing protein inter-action data with known complexes. Science. 302, 449-453 (2003).CrossRefMathSciNetGoogle Scholar
  7. [7]
    Bader et al.: A Bayesian Networks Approach for Predicting Protein-Protein Inter-actions from Genomic Data. Nature Biotechnology. 22 (1), 78-85 (2004).CrossRefGoogle Scholar
  8. [7]
    Dean et al.: Gaining con dence in high-throughput protein interaction networks. Mol Cel Proteom. 1 (5), 349-56 (2002).CrossRefGoogle Scholar
  9. [8]
    Xenarios et al.: Protein interactions: two methods for assessment of the reliability of high-throughput observations. NAR. 30 (1), 303-305 (2002).CrossRefGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Enrico Capobianco
    • 1
  • Elisabetta Marras
    • 1
  1. 1.CRS4 Bioinformatics LaboratoryTechnology Park of SardiniaItaly

Personalised recommendations