title: Learning robust cell signalling models from high throughput proteomic data.
abstract: We propose a framework for learning robust Bayesian network models of cell signalling from high-throughput proteomic data. We show that model averaging using Bayesian bootstrap resampling generates more robust structures than procedures that learn structures using all of the data. We also develop an algorithm for ranking the importance of network features using bootstrap resample data. We apply our algorithms to derive the T-cell signalling network from the flow cytometry data of Sachs et al. (2005). Our learning algorithm has identified, with high confidence, several new crosstalk mechanisms in the T-cell signalling network. Many of them have already been confirmed experimentally in the recent literature and six new crosstalk mechanisms await experimental validation.
authors.affiliation: Department of Computer Science, Rice University, Houston, TX 77005, USA. mkoch@rice.edu
authors.forename: Mitchell
authors.initials: M
authors.lastname: Koch
authors.forename: Bradley M
authors.initials: BM
authors.lastname: Broom
authors.forename: Devika
authors.initials: D
authors.lastname: Subramanian
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005434:Flow Cytometry; D040901:Proteomics; D015398:Signal Transduction; D013601:T-Lymphocytes / Q000378:metabolism*