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A Systems Biology Approach to BloodSystems Analysis of High-Throughput Data

A Systems Biology Approach to Blood: Systems Analysis of High-Throughput Data [Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Systems Biology Approach to BloodSystems Analysis of High-Throughput Data

Part of the Advances in Experimental Medicine and Biology Book Series (volume 844)
Editors: Corey, Seth Joel; Kimmel, Marek; Leonard, Joshua N.
Braun, Rosemary
Springer Journals — Dec 6, 2014
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34 pages

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Publisher
Springer New York
Copyright
© Springer Science+Business Media New York 2014
ISBN
978-1-4939-2094-5
Pages
153 –187
DOI
10.1007/978-1-4939-2095-2_8
Publisher site
See Chapter on Publisher Site

Abstract

[Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.]

Published: Dec 6, 2014

Keywords: Statistical analysis; High-throughput data; Microarrays; Sequencing; NGS; Genomics; Machine learning; Network models

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