> Dear AllStat,
>
> A new volume giving an overview of state of the art methods for
> inference in Computational Systems Biology is now available from MIT
> Press. For the volume we brought together researchers in statistics,
> computational biology, machine learning and systems biology. A more
> detailed synopsis is given below.
>
> Drafts of the text have already been used as a basis for teaching two
> separate courses in the area.
>
> http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12092
>
> Learning and Inference in Computational Systems Biology
> Edited by Neil D. Lawrence, Mark Girolami, Magnus Rattray and Guido
> Sanguinetti
>
> 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 in Computational Systems Biology provide a
> foundation upon which the next decade of research in systems biology
> can be built.
>
> Contributors: Florence d'Alch e-Buc, John Angus, Matthew J. Beal,
> Nicholas Brunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew
> Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li,
> Kuang Lin, Pedro Mendes, Nicholas A. M. Monk, Eric Mjolsness, Manfred
> Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido
> Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild,
> Darren Wilkinson, Guy Yosiphon
>
> --
>
>
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Professor M.A. Girolami F.I.E.T
EPSRC Advanced Research Fellow
Department of Computing Science
Sir Alwyn Williams Building, Room 302
University of Glasgow
Glasgow, G12 8QQ
Scotland UK
Tel : +44 (0)141 330 1623
Fax: +44 (0)141 330 2673
email : [log in to unmask]
web: http://www.dcs.gla.ac.uk/~girolami
web: http://www.dcs.gla.ac.uk/inference
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>
The University of Glasgow, charity number SC004401
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