Peptide identification via constrained multi-objective optimization: Pareto-based genetic algorithms

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...

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Veröffentlicht in:Concurrency and computation Jg. 17; H. 14; S. 1687 - 1704
Hauptverfasser: Malard, J. M., Heredia-Langner, A., Cannon, W. R., Mooney, R., Baxter, D. J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 10.12.2005
Schlagworte:
data-intensive computation
genetic algorithms
multiobjective optimization
peptide identification
tandem mass spectrometry
ISSN:1532-0626, 1532-0634
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Zusammenfassung: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.
Bibliographie:Office of Biological and Environmental Research in the U.S. Department of Energy - No. 41966A
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ArticleID:CPE953
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.953