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Modeling In Computational Biology And Biomedicine

A Multidisciplinary Endeavor

Modeling In Computational Biology And Biomedicine - Cazals, Frederic (EDT)/ Kornprobst, Pierre (EDT)/ Faugeras, Oliver (FRW)/ Janin, Joel (FRW) - ISBN: 9783642312076
Prijs: € 82,55
Levertijd: 4 tot 6 werkdagen
Bindwijze: Boek, Gebonden
Genre: Informatiekunde
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Beschrijving

Computational Biology, Mathematical Biology, Biology And Biomedicine Are Currently Undergoing Spectacular Progresses Due To A Synergy Between Technological Advances And Inputs From Physics, Chemistry, Mathematics, Statistics And Computer Science.

Details

Titel: Modeling In Computational Biology And Biomedicine
auteur: Cazals, Frederic (EDT)/ Kornprobst, Pierre (EDT)/ Faugeras, Oliver (FRW)/ Janin, Joel (FRW)
Mediatype: Boek
Bindwijze: Gebonden
Taal: Engels
Aantal pagina's: 318
Uitgever: Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg
Plaats van publicatie: DE
NUR: Informatiekunde
Afmetingen: 241 x 163 x 23
Gewicht: 654 gr
ISBN/ISBN13: 9783642312076
Intern nummer: 22413275

Recensie

From the reviews: "'A principal goal of this book is to illustrate that in modeling biological systems, deeper insights can be gained using more advanced mathematical and algorithmic developments that implicate a wide spectrum of techniques from applied mathematics and computer science.' The book achieves this goal. ... this is a well-organized and well-written book, with timely information for multidisciplinary researchers in the bioinformatics, biomedical signal and image analysis, and neuroscience modeling fields." (Jindong Liu, Computing Reviews, April, 2013)

Inhoudsopgave

Foreword by Olivier Faugeras.- Foreword by Joël Janin.- Preface.- Part I Bioinformatics.- 1.Modeling Macro-molecular Complexes: a Journey Across Scales. F.Cazals, T.Dreyfus, and C.H. Robert.- 1.1.Introduction.- 1.2.Modeling Atomic Resolution.- 1.3.Modeling Large Assemblies.- 1.4.Outlook.- 1.5.Online Resources.- References.- 2.Modeling and Analysis of Gene Regulatory Networks. G.Bernot, J-P.Comet, A.Richard, M.Chaves, J-L.Gouzé, and F.Dayan.- 2.1.Introduction.- 2.2.Continuous and Hybrid Models of Genetic Regulatory Networks.- 2.3.Discrete Models of GRN.- 2.4.Outlook.- 2.5.Online Resources.- 2.6.Acknowledgments.- References.- Part II Biomedical Signal and Image Analysis.- 3.Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques. V.Zarzoso, O.Meste, P.Comon, D.G.Latcu, and N.Saoudi.- 3.1.Preliminaries and Motivation.- 3.2.T-Wave Alternans Detection via Principal Component Analysis.- 3.3.Atrial Activity Extraction via Independent Component Analysis.- 3.4.Conclusion and Outlook.- 3.5.Online Resources.- References.- 4.Deconvolution and Denoising for Confocal Microscopy. P.Pankajakshan, G.Engler, L.Blanc-Féraud, and J.Zerubia.- 4.1.Introduction.- 4.2.Development of the Auxiliary Computational Lens.- 4.3.Outlook.- 4.4.Selected Online Resources.- References.- 5.Statistical Shape Analysis of Surfaces in Medical Images Applied to the Tetralogy of Fallot Heart. K.McLeod, T.Mansi, M.Sermesant, G.Pongiglione, and X.Pennec.- 5.1.Introduction.- 5.2.Statistical Shape Analysis.- 5.3.Shape Analysis of ToF Data.- 5.4.Conclusion.- 5.5.Online Resources.- References.- 6.From Diffusion MRI to Brain Connectomics. A.Ghosh and R.Deriche.- 6.1.Introduction.- 6.2.A Brief History of NMR and MRI.- 6.3.Nuclear Magnetic Resonance and Diffusion.- 6.4.From Diffusion MRI to Tissue Microstructure.- 6.5.Computational Framework for Processing Diffusion MR Images.- 6.6.Tractography: Inferring the Connectivity.- 6.7.Clinical Applications 6.8.Conclusion.- 6.9.Online Resources.- References.- Part III Modeling in neuroscience.- 7.Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information. M.Clerc, T.Papadopoulo, and C.Bénar.- 7.1.Introduction.- 7.2.Data-driven Approaches: Non-linear Dimensionality Reduction.- 7.3.Model-Driven Approaches: Matching Pursuit and its Extensions.- 7.4.Success Stories.- 7.5.Conclusion.- 7.6.Selected Online Resources.- References.- 8 Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina. B.Cessac and A.Palacios.- 8.1.Introduction.- 8.2.Unraveling the Neural Code in the Retina via Spike Train Statistics Analysis.- 8.3.Spike Train Statistics from a Theoretical Perspective.- 8.4.Using Gibbs Distributions to Analysing Spike Trains Statistics.- 8.5.Conclusion.- 8.6.Outlook.- 8.7.Online Resources.- References.- Biology, Medicine and Biophysics.- Mathematics and Computer Science.- Index.

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