Biocomputing

Inbunden, Engelska, 2002

AvP. M. Pardalos,Panos M. Paralos,Panos M. Pardalos,J.C. Principe

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In the quest to understand and model healthy or sick human body functions, researchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is advancing the development of the new field of biomedical computing, which is an interface between signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. This volume contains a collection of refereed papers. Included are contributions in genomics, global optimization in biomedicine, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics.

Produktinformation

Utgivningsdatum2002-06-30
Mått156 x 234 x 20 mm
Vikt588 g
FormatInbunden
SpråkEngelska
SerieBiocomputing
Antal sidor264
Upplaga2002
FörlagKluwer Academic Publishers
ISBN9781402006418

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1 Making Sense of Brain Waves: The Most Baffling Frontier in Neuroscience.- 1.1 Introduction.- 1.2 Relations between EEG and ‘units’.- 1.3 Three levels of hierarchical coding.- 1.4 Simulation of “background” activity.- 1.5 Microscopic coding and noise.- 1.6 Chaotic attractor stabilization and classification enhancement by noise.- 1.7 Mesoscopic to macroscopic interface.- 1.8 Summary.- References.- 2 Computational and Interpretive Genomics.- References.- 3 Optimized Needle Biopsy Strategies for Prostate Cancer Detection.- 3.1 Reconstruction of the prostate models.- 3.2 The Statistical distribution map.- 3.3 The Optimization problem.- 3.4 Optimized protocols.- 3.5 Conclusions and future work.- References.- 4 Phase Entrainment and Predictability of Epileptic Seizures.- 4.1 Introdution.- 4.2 Nonlinear dynamical measures.- 4.3 Selection of brain sites: optimization.- 4.4 Predictability analysis.- 4.5 Predictability results.- 4.6 Conclusions.- References.- 5 Self-Organizing Maps.- 5.1 The Self-Organizing Map Algorithm.- 5.2 Related Statistical Algorithms: A Qualitative Comparison.- 5.3 Background of Decoding Auditory Recordings.- 5.4 Relating the spike Pattern of Auditory Neurons to the Sound Stimuli using SOM.- 5.5 Conclusions and Discussions.- References.- 6 Finding Transition States Using the LTP Algorithm.- 6.1 Introduction.- 6.2 Theoretical background.- 6.3 The LTP procedure.- 6.4 Results and discussion.- 6.5 Concluding remarks.- References.- 7 A Simple Approximation Algorithm for Nonoverlapping Local Alignments (Weighted Independent Sets of Axis Parallel Rectangles).- 7.1 Introduction.- 7.2 Application of the Two-Phase technique to the IR problem.- 7.3 Concluding remarks.- 8 Combined Application of Global Optimization and Nonlinear Dynamics to Detect StateResetting in Human Epilepsy.- 8.1 Introdution.- 8.2 Nonlinear dynamical measures.- 8.3 Zero-one global optimization.- 8.4 Statistical testing of the resetting hypotheses.- 8.5 Conclusion.- References.- 9 functional Magnetic Resonance Imaging Data Analysis with Information-theoretic Approaches.- 9.1 Information-theoretic approaches.- 9.2 Two alternative divergence measures.- 9.3 fMRI neural activation study.- 9.4 Discussion.- 9.5 Summary.- References.- 10 Yeast SAGE Expression Levels are Related to Calculated mRNA Folding Free Energies.- References.- 11 Sources and Sinks in Medical Image Analysis.- 11.1 Introduction.- 11.2 Divergence-based skeletons.- 11.3 Flux maximizing flows.- 11.4 Conclusions.- References.- 12 Classical and Quantum Controlled Lattices: Self-Organization, Optimiza-tion and Biomedical Applications.- 12.1 Introduction.- 12.2 Hamiltonian models of the cellular dynamatons.- 12.3 Self-organization of the neural networks.- 12.4 Bilinear lattices and epileptic seizures.- 12.5 Quantum model of neural networks.- 12.6 Concluding remarks.- References.- 13 Computational Methods for Epilepsy Diagnosis. Visual Perception and EEG.- 13.1 Introduction.- 13.2 Visual perception tests.- 13.3 Data interpretation methods.- 13.4 EEG analysis.- 13.5 LPC and CHARADE interpretation.- 13.6 Conclusions.- References.- 14 Hardness and the Potential Energy Function in Internal Rotations: A Generalized Symmetry-Adapted Interpolation Procedure.- 14.1 Introduction.- 14.2 Theoretical considerations.- 14.3 Applications.- 14.4 Conclusions.- References.