Molecular & Cellular Proteomics
Volume 10, Issue 11, November 2011, M110.003384
Technological Innovation and ResourcesCONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches*
Under a Creative Commons license
open access
Cited by (0)
- *
This work was supported by the Biotechnology and Biological Sciences Research Council, via several grants to SJH and SJG (BB/F004605/1, BB/G009058/1, BB/C007735/1) and the Engineering and Physical Sciences Research Council (EP/D013615/1). CE is supported by a Royal Society Dorothy Hodgkin Research Fellowship.
This article contains supplemental Table S1 and Figs. S1 and S2.
- **
These authors contributed equally to this work.
© 2011 ASBMB. Currently published by Elsevier Inc; originally published by American Society for Biochemistry and Molecular Biology.