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Learning and Inference in Computational Systems Biology
Author: Neil D. Lawrence

ISBN: 9780262013864
Format: Hardcover
Number Of Pages: 376
Published: 13 February 2010
Country of Publication: US
Dimensions (cm): 22.86 x 17.78 x 2.54
Description:
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model--in other words, to answer specific questions about the underlying mechanisms of a biological system--in a process that can be thought of as "learning" or "inference." This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in "Learning and Inference ...

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