Biocomputing: (1 Biocomputing)

In the quest to understand and model the healthy or sick human body, re­ searchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is pushing the envelope of a new field we call Biomedical Computing, as an exciting frontier among signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. A conference on Biocomputing was held during February 25-27, 2001 at the University of Florida. The conference was sponsored by the Center for Applied Optimization, the Computational Neuroengineering Center, the Biomedical En­ gineering Program (through a Whitaker Foundation grant), the Brain Institute, the School of Engineering, and the University of Florida Research & Graduate Programs. The conference provided a forum for researchers to discuss and present new directions in Biocomputing. The well-attended three days event was highlighted by the presence of top researchers in the field who presented their work in Biocomputing. This volume contains a selective collection of ref­ ereed papers based on talks presented at this conference. You will find seminal contributions in genomics, global optimization, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics. We would like to take the opportunity to thank the sponsors, the authors of the papers, the anonymous referees, and Kluwer Academic Publishers for making the conference successful and the publication of this volume possible. Panos M. Pardalos and Jose C.

Table of Contents:
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.