AAAI Publications, Twelfth International Conference on the Principles of Knowledge Representation and Reasoning

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Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming
Martin Gebser, Carito Guziolowski, Mihail Ivanchev, Torsten Schaub, Anne Siegel, Sven Thiele, Philippe Veber

Last modified: 2010-04-27


We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to re-establish their mutual consistency-an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.

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