a boolean approach to linear prediction for signaling network modeling一个布尔值线性预测信号网络建模方法.pdfVIP

A Boolean Approach to Linear Prediction for Signaling Network Modeling Federica Eduati, Alberto Corradin, Barbara Di Camillo, Gianna Toffolo* Department of Information Engineering, University of Padova, Padova, Italy Abstract The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) ‘‘Predictive signaling network modeling’’ challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single- stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases. Citation: Eduati F, Corradin A, Di Camillo B, Toffolo G (2010) A Boolean Approach to Linear Prediction for Signaling Network Modeling. PLoS ONE 5(9): e12789. doi:10.1371/journal.pone.0012789 Editor: Mark Isalan, Center for Genomic Regulation, Spain Received March 31, 2010; Accepted July 14, 2010; Published September 16, 2010 Copyright: 2010 Eduati et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distributio