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Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data

Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data

Madhusudan Natarajan
Copyright: © 2011 |Pages: 18
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch015
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MLA

Natarajan, Madhusudan. "Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 337-354. https://doi.org/10.4018/978-1-60960-491-2.ch015

APA

Natarajan, M. (2011). Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 337-354). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch015

Chicago

Natarajan, Madhusudan. "Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 337-354. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch015

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Abstract

The inference of cellular architectures from detailed time-series measurements of intracellular variables is an active area of research. High throughput measurements of responses to cellular perturbations are usually analyzed using a variety of machine learning methods that typically only work within one type of measurement. Here, summaries of some recent research attempts are presented–these studies have expanded the scope of the problem by systematically integrating measurements across multiple layers of regulation including second messengers, protein phosphorylation markers, transcript levels, and functional phenotypes into signaling vectors or signatures of signal transduction. Data analyses through simple unsupervised methods provide rich insight into the biology of the underlying network, and in some cases reconstruction of key architectures of the underlying network from perturbation data. The methodological advantages provided by these efforts are examined using data from a publicly available database of responses to systematic perturbations of cellular signaling networks generated by the Alliance for Cellular Signaling (AfCS).

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