The use of semiparametric mixed models to analyze PamChip® peptide array data: an application to an oncology experiment
Author(s) -
Pushpike Thilakarathne,
Lieven Clement,
Dan Lin,
Ziv Shkedy,
Adetayo Kasim,
Willem Talloen,
Matthias Versele,
Geert Verbeke
Publication year - 2011
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btr475
Subject(s) - pointwise , computer science , kinase , computational biology , function (biology) , bioinformatics , biology , data mining , mathematics , microbiology and biotechnology , mathematical analysis
Phosphorylation by protein kinases is a central theme in biological systems. Aberrant protein kinase activity has been implicated in a variety of human diseases (e.g. cancer). Therefore, modulation of kinase activity represents an attractive therapeutic approach for the treatment of human illnesses. Thus, identification of signature peptides is crucial for protein kinase targeting and can be achieved by using PamChip(®) microarray technology. We propose a flexible semiparametric mixed model for analyzing PamChip(®) data. This approach enables the estimation of the phosphorylation rate (Velocity) as a function of time together with pointwise confidence intervals.
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