20.500.12556/RUL-125563 Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks Background: Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models. Results: We propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small. Conclusions: With the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology. fuzzy logic modelling and simulation data-driven modelling cellular networks computational biology model validation dynamic modelling MAPK signalling pathway circadian clock mehka logika modeliranje in simulacija podatkovno vodeno modeliranje celična omrežja računska biologija true false true Angleški jezik Slovenski jezik Članek v reviji 2021-03-25 13:34:51 2021-03-25 13:34:51 2022-09-05 03:51:22 0000-00-00 00:00:00 2018 0 0 7 str. art. 333 Vol. 19 Sep. 2018 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.94:57 1471-2105 10.1186/s12859-018-2366-0 1537917379 2433556 RAZ_Magdevska_Lidija_2018.pdf RAZ_Magdevska_Lidija_2018.pdf 1 D637A8721B2B408F41AC3BCB52E0ED81 15b0c709964be3650b5d0327d84a2dac5d71a0305487eedd47c862ba48e7c7d3 bbe07bde-a1bb-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=141322 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2366-0 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=141321 Fakulteta za računalništvo in informatiko Fakulteta za matematiko in fiziko 0 0 0