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Peptide detectability following ESI mass spectrometry: prediction using genetic programming by David C. Wedge; Simon J. Gaskell; Simon J. Hubbard; Douglas B. Kell; King Wai Lau; Claire Eyers is a scholarly article available to read on EtoBox.

What is Peptide detectability following ESI mass spectrometry: prediction using genetic programming about?

The accurate quantification of proteins is important in several areas of cell biology, biotechnology and medicine. Both relative and absolute quantification of proteins is often determined following mass spectrometric analysis of one or more of their constituent peptides. However, in order for quantification to be successful, it is important that the experimenter knows which peptides are readily detectable under the mass spectrometric conditions used for analysis. In this paper, genetic programming is used to develop a function which predicts the detectability of peptides from their calculated physico-chemical properties. Classification is carried out in two stages: the selection of a good classifier using the AUROC objective function and the setting of an appropriate threshold. This allows the user to select the balance point between conflicting priorities in an intuitive way. The success of this method is found to be highly dependent on the initial selection of input parameters. The use of brood recombination and a modified version of the multi-objective FOCUS method are also investigated. While neither has a significant effect on predictive accuracy, the use of the FOCUS method

Author

David C. Wedge; Simon J. Gaskell; Simon J. Hubbard; Douglas B. Kell; King Wai Lau; Claire Eyers

Publisher

ACM

Published

2007

Language

EN