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Novel Angiogenic Functional Targets Predicted through “Dark Matter” Assessment in Protein Networks

  • Ian Morilla
  • Miguel A. Medina
  • Juan A. G. Ranea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6620)

Abstract

In order to model protein networks we must extend our knowledge of the protein associations occurring in molecular systems and their functional relationships. We have significantly increased the accuracy of protein association predictions by the meta-statistical integration of three computational methods specifically designed for eukaryotic proteomes. From this former work it was discovered that high-throughput experimental assays seem to perform biased screenings of the real protein networks and leave important areas poorly characterized. This finding supports the convenience to combine computational prediction approaches to model protein interaction networks. We address in this work the challenge of integrating context information, present in predicted and known protein network models, to functionally characterize novel proteins. We applied a random walk-with-restart kernel to our models aiming at fixing some poorly described or unknown proteins involve in angiogenesis. This approach reveals some novel key angiogenic components within the human interactome.

Keywords

protein domain fusions ppi networks functional prediction proteins networks topology genes candidate prioritization and random walks with restart 

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References

  1. 1.
    Köhler, S., Bauer, S., Horn, D., Robinson, P.N.: Walking the Interactome for Prioritization of Candidate Disease Genes. The American Society of Human Genetics (2008), doi:10.1016/j.ajhg.2008.02.013Google Scholar
  2. 2.
    Ranea, J.A.G., Morilla, I., Lees, J.G., Reid, A.J., Yeats, C., Clegg, A.B., Sanchez-Jimenez, F., Orengo, C.: Finding the ”Dark Matter” in Human and Yeast Protein Network Prediction and Modelling. PLoS Comput. Biol. 6(9), e1000945 (2010), doi:10.1371/journal.pcbi.1000945CrossRefGoogle Scholar
  3. 3.
    Fouss, F., Franoisse, K., Yen, L., Pirotte, A., Saerens, M.: An Experimental Investigation of Graph Kernels on Collaborative Recommendation and Semisupervised Classification. In: Proceedings of the Eighth International Conference on Data Mining, ICDM 2009 (2009)Google Scholar
  4. 4.
    Li, Y., Patra, J.C.: Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics 11(suppl. 1), S20 (2010), doi:10.1186/1471-2105-11-S1-S20CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ian Morilla
    • 1
  • Miguel A. Medina
    • 1
    • 2
  • Juan A. G. Ranea
    • 1
    • 2
  1. 1.Department of Molecular Biology and BiochemistryUniversity of MalagaMalagaSpain
  2. 2.CIBER de Enfermedades Raras (CIBERER)ValenciaSpain

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