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A Preliminary Study on the Prediction of Human Protein Functions

  • Guido Bologna
  • Anne-Lise Veuthey
  • Marco Pagni
  • Lydie Lane
  • Amos Bairoch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

In the human proteome, about 5’000 proteins lack experimentally validated functional information. In this work we propose to tackle the problem of human protein function prediction by three distinct supervised learning schemes: one-versus-all classification; tournament learning; multi-label learning. Target values of supervised learning models are represented by the nodes of a subset of the Gene Ontology, which is widely used as a benchmark for functional prediction. With an independent dataset including very difficult cases the recall measure reached a reasonable performance for the first 50 ranked predictions, on average; however, average precision was quite low.

Keywords

Gene Ontology Average Precision Average Recall Swiss Institute Predict Protein Function 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guido Bologna
    • 1
  • Anne-Lise Veuthey
    • 2
  • Marco Pagni
    • 3
  • Lydie Lane
    • 1
    • 4
  • Amos Bairoch
    • 1
    • 4
  1. 1.CALIPHO Group, Swiss Institute of BioinformarticsGeneva 4Switzerland
  2. 2.Swiss-Prot Group, Swiss Institute of BioinformarticsGeneva 4Switzerland
  3. 3.Vital-IT Group, Swiss Institute of BioinformarticsQuartier SorgeGenopodeSwitzerland
  4. 4.Department of Structural Biology and BioinformaticsUniversity of GenevaGeneva 4Switzerland

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