The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed. This volume, along with previous and forthcoming Computer Methods volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research.…mehr
The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed. This volume, along with previous and forthcoming Computer Methods volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research.
1. Correlation Analysis: A New Tool for Comparing Relaxation-Type Models to Experimental Data Maurizio Tomaiuolo, Joel Tabak, Richard Bertram 2. Mathematical modeling of cancer Vito Quaranta3. Pattern recognition in biological systems Michael Ochs4. Modeling and simulations of the immune system as a self regulating network Peter P. Lee, Peter S. Kim and Doron Levy5. Entropy Demystified: The Thermodynamics of Stochastically Fluctuating Systems Hong Qian6. Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates 9P. Holland Alday, Walter F Stafford and John J Correia7. High Throughput Computing in the Sciences Mark Morgan and Andrew Grimshaw8. Traffic theory applications in biology Steven Pincus9. Large scale transcriptome data integration across multiple tissues to decipher stem cell signatures Ghislain Bidaut10. DynaFit Peter Kuzmic11. Discrete dynamic modeling of signaling networks Reka Albert and Ruisheng Wang12. Fitting kinetic data by computer simulation methods Kenneth A. Johnson13. Molecular modeling David Sept14. Data integration and analysis of transcriptional networks Ilya Shmulevich and John D. Aitchison15. A Bayesian probability approach to ADHD appraisal Raina Robeva16. Enzyme kinetics parameter estimation using numerical simulation and optimization Pedro Mendes17. Modelling partial hormonal feedback networks Leon Farhy, William S. Evans and Michael L Johnson
1. Correlation Analysis: A New Tool for Comparing Relaxation-Type Models to Experimental Data Maurizio Tomaiuolo, Joel Tabak, Richard Bertram 2. Mathematical modeling of cancer Vito Quaranta3. Pattern recognition in biological systems Michael Ochs4. Modeling and simulations of the immune system as a self regulating network Peter P. Lee, Peter S. Kim and Doron Levy5. Entropy Demystified: The Thermodynamics of Stochastically Fluctuating Systems Hong Qian6. Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates 9P. Holland Alday, Walter F Stafford and John J Correia7. High Throughput Computing in the Sciences Mark Morgan and Andrew Grimshaw8. Traffic theory applications in biology Steven Pincus9. Large scale transcriptome data integration across multiple tissues to decipher stem cell signatures Ghislain Bidaut10. DynaFit Peter Kuzmic11. Discrete dynamic modeling of signaling networks Reka Albert and Ruisheng Wang12. Fitting kinetic data by computer simulation methods Kenneth A. Johnson13. Molecular modeling David Sept14. Data integration and analysis of transcriptional networks Ilya Shmulevich and John D. Aitchison15. A Bayesian probability approach to ADHD appraisal Raina Robeva16. Enzyme kinetics parameter estimation using numerical simulation and optimization Pedro Mendes17. Modelling partial hormonal feedback networks Leon Farhy, William S. Evans and Michael L Johnson
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