- author = "David C. Wedge and Simon J. Gaskell and Simon J. Hubbard and Douglas B. Kell and King Wai Lau and Claire Eyers",
- title = "Peptide detectability following ESI mass spectrometry: prediction using genetic programming",
- booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation",
- year = "2007",
- 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",
- volume = "2",
- isbn13 = "978-1-59593-697-4",
- pages = "2219--2225",
- address = "London",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2219.pdf",
-
DOI = "
doi:10.1145/1276958.1277382",
- publisher = "ACM Press",
- publisher_address = "New York, NY, USA",
- month = "7-11 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, Real-World Applications, AUROC, input selection, mass spectrometry, proteomics",
- 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.",
-
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.",
