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