Probabilistic paths for protein complex inference

  • Authors:

  • Hailiang Huang;Lan V. Zhang;Frederick P. Roth;Joel S. Bader

  • Affiliations:

  • Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD and High-Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD;Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA;Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA;Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD and High-Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD

  • Venue:
  • RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics
  • Year:
  • 2006

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Abstract

Understanding how individual proteins are organized into complexes and pathways is a significant current challenge. We introduce new algorithms to infer protein complexes by combining seed proteins with a confidence-weighted network. Two new stochastic methods use averaging over a probabilistic ensemble of networks, and the new deterministic method provides a deterministic ranking of prospective complex members. We compare the performance of these algorithms with three existing algorithms. We test algorithm performance using three weighted graphs: a naïve Bayes estimate of the probability of a direct and stable protein-protein interaction; a logistic regression estimate of the probability of a direct or indirect interaction; and a decision tree estimate of whether two proteins exist within a common protein complex. The best-performing algorithms in these trials are the new stochastic methods. The deterministic algorithm is significantly faster, whereas the stochastic algorithms are less sensitive to the weighting scheme.