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Peptide identification via constrained multi‐objective optimization: Pareto‐based genetic algorithms
Author(s) -
Malard J. M.,
HerediaLangner A.,
Can W. R.,
Mooney R.,
Baxter D. J.
Publication year - 2005
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.953
Subject(s) - pareto principle , independence (probability theory) , computer science , probabilistic logic , identification (biology) , algorithm , mathematical optimization , population , selection (genetic algorithm) , fuzzy logic , multi objective optimization , matching (statistics) , mathematics , artificial intelligence , machine learning , biology , statistics , botany , demography , sociology
Abstract Automatic peptide identification from collision‐induced dissociation tandem mass spectrometry data using optimization techniques is made difficult by large plateaus in the fitness landscapes of scoring functions, by the fuzzy nature of constraints from noisy data and by the existence of diverse but equally justifiable probabilistic models of peak matching. Here, two different scoring functions are combined into a parallel multi‐objective optimization framework. It is shown how multi‐objective optimization can be used to empirically test for independence between distinct scoring functions. The loss of selection pressure during the evolution of a population of putative peptide sequences by a Pareto‐driven genetic algorithm is addressed by alternating between two definitions of fitness according to a numerical threshold. Copyright © 2005 John Wiley & Sons, Ltd.