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Reverse engineering biological networks : opportunities and challenges in computational methods for pathway inference

Author: Gustavo Stolovitzky; Andrea Califano
Publisher: Malden, MA : Published on behalf of the New York Academy of Sciences by Blackwell Pub., 2007.
Series: Annals of the New York Academy of Sciences, v. 1115.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Database:WorldCat
Summary:

Computational biologists are striving to "reverse engineer" the underlying networks of interactions between the molecules in the cell. This volume and the conference it reports on attempt a  Read more...

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Genre/Form: Congresses
Conference papers and proceedings
Additional Physical Format: Print version:
Workshop for Dialogue on Reverse Engineering Assessment and Methods (2006 : New York, N.Y.).
Reverse engineering biological networks.
Boston, Mass. : Published by Blackwell Pub. on behalf of the New York Academy of Sciences, ©2007
(DLC) 2007039308
(OCoLC)173368437
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Gustavo Stolovitzky; Andrea Califano
OCLC Number: 184845418
Notes: This volume is the result of a conference entitled Workshop for Dialogue on Reverse Engineering Assessment and Methods (DREAM) held September 7-8, 2006 at Wave Hill, New York.
Description: 1 online resource (xiv, 287 pages) : illustrations (some color).
Contents: Dialogue on reverse-engineering assessment and methods : the DREAM of high-throughput pathway inference / Gustavo Stolovitzky, Don Monroe, and Andrea Califano --
ENFIN, a network to enhance integrative systems biology / Pascal Kahlem and Ewan Birney --
Reconstructing signal transduction pathways : challenges and opportunities / Arnold J. Levine [and others] --
Theory and limitations of genetic network inference from microarray data / Adam A. Margolin and Andrea Califano --
Comparison of reverse-engineering methods using an in silico network / Diogo Camacho [and others] --
Benchmarking of dynamic Bayesian networks inferred from stochastic time-series data / Lawrence A. David and Chris H. Wiggins --
Reconstruction of metabolic networks from high-throughput metabolite profiling data : in silico analysis of red blood cell metabolism / Ilya Nemenman [and others] --
The gap gene system of Drosophila melanogaster : model-fitting and validation / Theodore J. Perkins --
Algorithmic issues in reverse engineering of protein and gene networks via the modular response analysis method / Piotr Berman, Bhaskar Dasgupta, and Eduardo Sontag --
Data requirements of reverse-engineering algorithms / Winfried Just --
Improving protein-protein interaction prediction based on phylogenetic information using a least-squares support vector machine / Roger A. Craig and Li Liao --
Reverse engineering of dynamic networks / B. Stigler [and others] --
Learning regulatory programs that accurately predict differential expression with MEDUSA / Anshul Kundaje [and others] --
Extracting falsifiable predictions from sloppy models / Ryan N. Gutenkunst [and others] --
Dynamic pathway modeling : feasibility analysis and optimal experimental design / Thomas Maiwald [and others] --
Sensitivity analysis of a computational model of the IKK-NF-[kappa]B-I[kappa]B[alpha]-A20 signal transduction network / Jaewook Joo [and others] --
A framework for elucidating regulatory networks based on prior information and expression data / Olivier Gevaert, Steven Van Vooren, and Bart De Moore --
CellFrame : a data structure for abstraction of cell biology experiments and construction of perturbation networks / Yunchen Gong and Zhaolei Zhang --
Alternative pathway approach for automating analysis and validation of cell perturbation networks and design of perturbation experiments / Yunchen Gong and Zhaolei Zhang.
Series Title: Annals of the New York Academy of Sciences, v. 1115.
Responsibility: edited by Gustavo Stolovitzky and Andrea Califano.

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“The volume particularly excels at its primary goal, which is to honestly describe the progress made in network construction and the challenges still facing researchers." (The Quarterly Review of Read more...

 
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