Qualitative reasoning about physical systems: a return to roots
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Qualitative reasoning on systematic gene perturbation experiments
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
GenePath: a system for inference of genetic networks and proposal of genetic experiments
Artificial Intelligence in Medicine
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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.