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Kinase Signaling Networks pp 523–533Cite as

Partial Least Squares Regression Models for the Analysis of Kinase Signaling

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1636))

Abstract

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

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Acknowledgments

This work was supported by grants from the American Cancer Society (RSG-13-026-01-CSM), NIH (1DP2CA195766, R01GM099031, R21CA202040, R21EY026222), and NSF (CBET-0951613, CBET-1401584).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA

    Danielle L. Bourgeois & Pamela K. Kreeger

Authors
  1. Danielle L. Bourgeois
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  2. Pamela K. Kreeger
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Corresponding author

Correspondence to Pamela K. Kreeger .

Editor information

Editors and Affiliations

  1. Division of Medical Oncology, University of Colorado, Aurora, Colorado, USA

    Aik-Choon Tan

  2. Protein Networks Laboratory, Institute of Cancer Research, London, United Kingdom

    Paul H. Huang

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Bourgeois, D.L., Kreeger, P.K. (2017). Partial Least Squares Regression Models for the Analysis of Kinase Signaling. In: Tan, AC., Huang, P. (eds) Kinase Signaling Networks. Methods in Molecular Biology, vol 1636. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7154-1_32

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  • DOI: https://doi.org/10.1007/978-1-4939-7154-1_32

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7152-7

  • Online ISBN: 978-1-4939-7154-1

  • eBook Packages: Springer Protocols

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