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008			151026s2016 gw | s |||| 0|eng d
020			_a9783319212968
040			_aES-MaUEC
050		4	_aQH323.5 _bU534 2016
082	0	4	_a610.28
245	1	0	_aUncertainty in Biology : _bA Computational Modeling Approach _cedited by Liesbet Geris, David Gomez-Cabrero
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (IX, 478 páginas) _b142 ilustraciones, 45 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aStudies in Mechanobiology, Tissue Engineering and Biomaterials _x1868-2006 _v17
505	0		_aAn Introduction to Uncertainty in the Development of Computational Models of Biological Processes -- Reverse Engineering under Uncertainty -- Probabilistic Computational Causal Discovery for Systems Biology -- Macroscopic Simulation of Individual-Based Stochastic Models for Biological Processes -- The Experimental Side of Parameter Estimation -- Statistical Data Analysis and Modeling -- Optimization in Biology: Parameter Estimation and the Associated Optimization Problem -- Interval Methods -- Model Extension and Model Selection -- Bayesian Model Selection Methods and their Application to Biological ODE Systems -- Sloppiness and the Geometry of Parameter Space -- Modeling and Model Simplification to Facilitate Biological Insights and Predictions -- Sensitivity Analysis by Design of Experiments -- Waves in Spatially-Disordered Neural Fields: a Case Study in Uncertainty Quantification -- X In-silico Models of Trabecular Bone: a Sensitivity Analysis Perspective -- Neuroswarm: a Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons -- Prediction Uncertainty Estimation Despite Unidentifiability: an Overview of Recent Developments -- Computational Modeling Under Uncertainty: Challenges and Opportunities.
520	3		_aComputational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.� Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.� This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.� This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aBioinformática _2embne _9160489
650	0	7	_aBioinformática _2embne _9160489
700	1		_aGeris, Liesbet _eeditor literario _0Local _997164
700	1		_aGomez-Cabrero, David _eeditor literario _0Local _997165
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-21296-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319212968
907			_a.b12940252 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQH323.5 U534 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11584695 _z06-04-17
999			_c84747 _d84747 _x1