@inproceedings{1277382,
	title = {Peptide detectability following {ESI} mass
                 spectrometry: prediction using genetic programming},
	address = {London},
	author = {David C. Wedge and Simon J. Gaskell and Simon J. Hubbard and Douglas B. Kell and King Wai Lau and Claire Eyers},
	booktitle = {GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation},
	editor = {Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener},
	month = {7-11 July},
	pages = {2219--2225},
	publisher = {ACM Press},
	url = {http://doi.acm.org/10.1145/1276958.1277382},
	volume = {2},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/283274d0fa809caec1aed77419dcd337d/brazovayeye},
	abstract = {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 leads to considerably more compact solutions.},
	organisation = {ACM SIGEVO (formerly ISGEC)}, publisher_address = {New York, NY, USA}, isbn13 = {978-1-59593-697-4}, notes = {GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071

                 Data lineraliy transformed to -1+1 range. Binary
                 classification, AUROC. feature selection 393, 34 or 6
                 inputs. PPV. QconCat.},
	keywords = {AUROC, Applications, Real-World algorithms, genetic input mass programming, proteomics selection, spectrometry, }
}