Handbook of Statistical Systems Biology
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters.
This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.
Was andere dazu sagen - Rezension schreiben
Es wurden keine Rezensionen gefunden.
Bayesian Inference and Computation
Data Integration Towards Understanding
Control Engineering Approaches toReverse Engineering Biomolecular Networks
Transcriptomic Technologies and Statistical Data
StatisticalData Analysis inMetabolomics 8 1 Introduction
Stochastic Dynamical Systems
Gaussian Process Inferencefor Differential Equation Models of Transcriptional Regulation
Model Identification by Utilizing Likelihood
1ODE Models forReaction Networks 20 2 Parameter Estimation
Introduction to Graphical Modelling
Recovering Genetic Network from Continuous
Advanced Applications of Bayesian Networks
3PPI network models 14 4Rangedependent graphs
Andere Ausgaben - Alle anzeigen
algorithm alignment analysis approach approximation Bayes Bayesian networks biochemical Bioinformatics canbe cell cellular changepoint classifier clustering coefficients complex components computational concentration conditional confidence intervals consider correlation corresponding covariance database datasets defined degree distribution depend dynamical systems edges etal example experimental data Figure function Gaussian gene expression gene regulatory networks genetic Genome graphical models Gröbner basis identified inference integration interactome inthe isthe kinetic likelihood linear Lyapunov exponent Markov mathematical matrix MCMC measurements metabolic metabolites metabolomics methods microarray microRNA molecular mRNA multiple network structure nodes nonidentifiability nonlinear numberof observed obtained ofthe onthe optimization pathogen pathway polynomial posterior distribution posterior probability PPI networks prediction prior distribution probability problem procedure profiles protein protein–protein interactions random graphs reaction regression regulation regulatory networks sample sequence signaling simulation specific stochastic systems biology target techniques tothe trajectories transcription factor values variables vector yeast