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RECOMB
2006
Springer

Probabilistic Paths for Protein Complex Inference

10 years 11 months ago
Probabilistic Paths for Protein Complex Inference
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 confidenceweighted 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 na?ve 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...
Hailiang Huang, Lan V. Zhang, Frederick P. Roth, J
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2006
Where RECOMB
Authors Hailiang Huang, Lan V. Zhang, Frederick P. Roth, Joel S. Bader
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