Format

Send to

Choose Destination
Methods Mol Biol. 2017;1636:523-533. doi: 10.1007/978-1-4939-7154-1_32.

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

Author information

1
Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA.
2
Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA. kreeger@wisc.edu.

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.

KEYWORDS:

Data-driven modeling; PLSR; Proteomics; Regression analysis; Systems biology

PMID:
28730500
DOI:
10.1007/978-1-4939-7154-1_32
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center