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Updated: Feb 26, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Partial Least Squares Regression Models for the Analysis of Kinase Signaling
Danielle L Bourgeois1, Pamela K Kreeger2
1Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA.
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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