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Learning and Inference in Computational Systems Biology (Computational Molecular Biology)

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Synopses & Reviews

Publisher Comments:

Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. 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

Computational Molecular Biology series

Synopsis:

Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific.

Synopsis:

andlt;Pandgt;Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific.andlt;/Pandgt;

Synopsis:

andlt;Pandgt;Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. 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. 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 Yosiphonandlt;/Pandgt;

Synopsis:

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

About the Author

Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. 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. 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

Product Details

ISBN:
9780262013864
Author:
Lawrence, Neil D.
Publisher:
The MIT Press
Editor:
Girolami, Mark
Editor:
Rattray, Magnus
Author:
Vyshemirsky, Vladislav
Author:
Husmeier, Dirk
Author:
Li, Juan
Author:
Brunel, Nicolas
Author:
Gao, Pei
Author:
Mendes, Pedro
Author:
Sanguinetti, Guido
Author:
Lin, Kuang
Author:
Wild, David L.
Author:
Jaeger, Johannes
Author:
Yosiphon, Guy
Author:
Angus, John
Author:
Rangel, Claudia
Author:
Opper, Manfred
Author:
Girolami, Mark
Author:
Titsias, Michalis
Author:
d'Alché-Buc, Florence
Author:
Massachusetts Institute of Technology
Author:
Wilkinson, Darren J.
Author:
Calderhead, Ben
Author:
Ruttor, Andreas
Author:
Golightly, Andrew
Author:
Beal, Matthew J.
Author:
Mjolsness, Eric
Author:
Rattray, Magnus
Author:
Monk, Nicholas A. M.
Location:
Cambridge
Subject:
Machine learning
Subject:
Inference
Subject:
General
Subject:
Life Sciences - Biology - General
Subject:
Bioinformatics
Subject:
Computers Reference-Bioinformatics
Copyright:
Edition Description:
New
Series:
Computational Molecular Biology Learning and Inference in Computational Systems Biology
Publication Date:
20091204
Binding:
Hardback
Grade Level:
from 17
Language:
English
Illustrations:
73 b, &, w illus., 17 tables
Pages:
376
Dimensions:
9 x 7 x 0.62 in

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Related Subjects

Computers and Internet » Computers Reference » Bioinformatics
Reference » Science Reference » General

Learning and Inference in Computational Systems Biology (Computational Molecular Biology) New Hardcover
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$47.75 Backorder
Product details 376 pages MIT Press (MA) - English 9780262013864 Reviews:
"Synopsis" by , Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific.
"Synopsis" by , andlt;Pandgt;Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific.andlt;/Pandgt;
"Synopsis" by , andlt;Pandgt;Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. 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. 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 Yosiphonandlt;/Pandgt;
"Synopsis" by , 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
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