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

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

By: Magnus Rattray (editor), Guido Sanguinetti (editor), Neil D. Lawrence (editor), Mark Girolami (editor)Hardback

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Description

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

About Author

Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester. Mark Girolami is Professor of Computing and Inferential Science in the Department of Computing Science and the Department of Statistics at the University of Glasgow. Magnus Rattray is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester. Guido Sanguinetti is Lecturer in Systems Biology jointly in the Department of Computer Science and Chemical Engineering at the Life Sciences Interface Institute in the Department of Chemical and Process Engineering, University of Sheffield. Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester. Magnus Rattray is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester. Mark Girolami is Professor of Computing and Inferential Science in the Department of Computing Science and the Department of Statistics at the University of Glasgow. Guido Sanguinetti is Lecturer in Systems Biology jointly in the Department of Computer Science and Chemical Engineering at the Life Sciences Interface Institute in the Department of Chemical and Process Engineering, University of Sheffield. Manfred Opper is a Reader at the Neural Computing Research Group, School of Engineering and Applied Science, Aston University, UK. Pavel Pevzner is Ronald R. Taylor Professor of Computer Science at the University of California, San Diego. He is the author of Computational Molecular Biology: An Algorithmic Approach (MIT Press, 2000).

Product Details

  • ISBN13: 9780262013864
  • Format: Hardback
  • Number Of Pages: 376
  • ID: 9780262013864
  • weight: 771
  • ISBN10: 026201386X

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