GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Ahmed:evoapps14,
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author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
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title = "GPMS: A Genetic Programming Based Approach to Multiple
Alignment of Liquid Chromatography-Mass Spectrometry
Data",
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booktitle = "17th European Conference on the Applications of
Evolutionary Computation",
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year = "2014",
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editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora",
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series = "LNCS",
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volume = "8602",
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publisher = "Springer",
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pages = "915--927",
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address = "Granada",
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month = "23-25 " # apr,
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-662-45522-7",
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DOI = "doi:10.1007/978-3-662-45523-4_74",
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abstract = "Alignment of samples from Liquid chromatography-mass
spectrometry (LC-MS) measurements has a significant
role in the detection of biomarkers and in metabolomic
studies.The machine drift causes differences between
LC-MS measurements, and an accurate alignment of the
shifts introduced to the same peptide or metabolite is
needed. In this paper, we propose the use of genetic
programming (GP) for multiple alignment of LC-MS data.
The proposed approach consists of two main phases. The
first phase is the peak matching where the peaks from
different LC-MS maps (peak lists) are matched to allow
the calculation of the retention time deviation. The
second phase is to use GP for multiple alignment of the
peak lists with respect to a reference. In this paper,
GP is designed to perform multiple-output regression by
using a special node in the tree which divides the
output of the tree into multiple outputs. Finally, the
peaks that show the maximum correlation after dewarping
the retention times are selected to form a consensus
aligned map.The proposed approach is tested on one
proteomics and two metabolomics LC-MS datasets with
different number of samples. The method is compared to
several benchmark methods and the results show that the
proposed approach outperforms these methods in three
fractions of the protoemics dataset and the
metabolomics dataset with a larger number of maps.
Moreover, the results on the rest of the datasets are
highly competitive with the other methods",
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notes = "EvoApplications2014 held in conjunction with
EuroGP'2014, EvoCOP2014, EvoBIO2014, and
EvoMusArt2014",
- }
Genetic Programming entries for
Soha Ahmed
Mengjie Zhang
Lifeng Peng
Citations