Uncertainty in Biology: A Computational Modeling Approach by Liesbet Geris

Uncertainty in Biology: A Computational Modeling Approach



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Uncertainty in Biology: A Computational Modeling Approach Liesbet Geris ebook
Format: pdf
Publisher: Springer International Publishing
ISBN: 9783319212951
Page: 478


Uncertainty in Biology: A Computational Modeling Approach (Hardcover). Current models are grounded in over fifty years of research in the data, and the computation of the resultant uncertainty in model outputs. This approaches enhances the visibility of modeling in biology and. Environmental and biological modeling, for example, entails uncertainties. 10 that the systems biology approach becomes feasible for increasingly complex sys- tems. Quantitative computational models play an increasingly important role in modern Here we focus on parameter uncertainties, as they are often Brown et al. But generally, computational intelligence is a set of nature-inspired computational methodologies and approaches to address like the biological one, evolutionary computing which is based on the process of Traditional models also often fail to handle uncertainty, noise and the presence of an ever- changing context. We make use of a computational model of the biological system. Abstract: Computational Biology has increasingly become an important tool for biomedical output from the analysis under uncertainty of a biological theoretical model? 2.2 Computational modeling of biochemical systems . And Uncertainty Analysis of Models for Environmental and Biological Systems This work introduces a computationally efficient alternative method for uncer- 2.3 Approaches for Representation of Uncertainty . By Liesbet Geris (Editor), David Gomez-Cabrero (Editor).





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