Title: Learning and inference in computational systems biology /
Authors: Lawrence, Neil
Publisher: MIT Press,
Year: 2010
Subjects: Bioinformatics Statistical methods.; Inference; Machine learning; Systems Biology; Bayes Theorem
Contents: Introduction / Neil D. Lawrence -- Reverse engineering of gene regulatory networks / Johannes Jaeger and Nicholas A.M. Monk -- Framework for comparative assessment of parameter estimation and inference methods in systems biology / Pedro Mendes -- Estimation of parametric nonlinear ODEs for biological networks identification / Florence d'Alché-Buc, Nicholas Brunel -- A brief introduction to Bayesian inference / Neil D. Lawrence, Magnus Rattray -- Inferring transcriptional networks using prior biological knowledge and constrained state-space models / John Angus [and others] -- Mixtures of factor analyzers for modeling transcriptional regulation / Kuang Lin, and Dirk Husmeier -- System identification and model ranking: the Bayesian perspective / Mark Girolami, Ben Calderhead and Vladislav Vyshemirsky -- Gaussian processes for missing species in biochemical systems / Neil D. Lawrence [and others] -- Markov chain Monte Carlo algorithms for SDE parameter estimation / Darren J. Wilkinson and Andrew Golightly -- Approximate inference for stochastic reaction processes / Andreas Ruttor, Guido Sanguinetti, and Manfred Opper -- Toward the inference of stochastic biochemical network and parameterized grammar models / Guy Yosiphon and Eric Mjolsness.