Techniques to cope with missing data in host-pathogen protein interaction prediction

Meghana Kshirsagar, Jaime Carbonell, Judith Klein-Seetharaman

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Motivation: Approaches that use supervised machine learning techniques for protein-protein interaction (PPI) prediction typically use features obtained by integrating several sources of data. Often certain attributes of the data are not available, resulting in missing values. In particular, our host-pathogen PPI datasets have a large fraction, in the range of 58-85% of missing values, which makes it challenging to apply machine learning algorithms. Results: We show that specialized techniques for missing value imputation can improve the performance of the models significantly. We use cross species information in combination with machine learning techniques like Group lasso with l1/l2 regularization. We demonstrate the benefits of our approach on two PPI prediction problems. In our first example of Salmonella-human PPI prediction, we are able to obtain high prediction accuracies with 77.6% precision and 84% recall. Comparison with various other techniques shows an improvement of 9 in F1 score over the next best technique. We also apply our method to Yersinia-human PPI prediction successfully, demonstrating the generality of our approach.

Original languageEnglish (US)
Article numberbts375
Pages (from-to)i466-i472
JournalBioinformatics
Volume28
Issue number18
DOIs
StatePublished - Sep 2012
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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