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Modeling in Computational Biology and Biomedicine A Multidisciplinary Endeavor /

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. The goal of this book is to evidence this synergy by...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Cazals, Frďřic. (Editor), Kornprobst, Pierre. (Editor)
Format: Electronic
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Subjects:
Mathematics.
Bioinformatics.
Mathematical and Computational Biology.
Applications of Mathematics.
Computational Biology/Bioinformatics.
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-31208-3
Table of Contents:
  • Foreword by Olivier Faugeras
  • Foreword by Jol͡ 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.Gouz,̌ 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-Fřaud, 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.Bňar
  • 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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