MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets.

ABSTRACT: Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typi

SUBMITTER: Naegle KM 

PROVIDER: S-EPMC3140961 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature