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Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
Sept.-Oct. 2012 (vol. 9 no. 5)
pp. 1482-1491
Silvana Badaloni, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Barbara Di Camillo, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
Francesco Sambo, Dept. of Inf. Eng., Univ. of Padova, Padova, Italy
The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.
Index Terms:
common-sense reasoning,bioinformatics,bioinformatics,biological network qualitative reasoning,causal relations,systematic perturbation-qualitative reasoning approach,IF-THEN rules,biological network,Systematics,Cognition,Biological information theory,Proteins,Biological system modeling,Mathematical model,protein signaling networks.,Qualitative reasoning,rule-based inference,systematic perturbation experiments,gene regulatory networks
Citation:
Silvana Badaloni, Barbara Di Camillo, Francesco Sambo, "Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 5, pp. 1482-1491, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.69
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