MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets

@inproceedings{Naegle2011MCAMMC,
  title={MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets},
  author={Kristen M. Naegle and Roy E. Welsch and Michael B. Yaffe and Forest M. White and Douglas A. Lauffenburger},
  booktitle={PLoS Computational Biology},
  year={2011}
}
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… CONTINUE READING
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