GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Ahmed:evoapps14,

• author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
• title = "GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data",
• booktitle = "17th European Conference on the Applications of Evolutionary Computation",
• year = "2014",
• editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora",
• series = "LNCS",
• volume = "8602",
• publisher = "Springer",
• pages = "915--927",
• address = "Granada",
• month = "23-25 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-662-45522-7",
• DOI = "doi:10.1007/978-3-662-45523-4_74",
• 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",
• notes = "EvoApplications2014 held in conjunction with EuroGP'2014, EvoCOP2014, EvoBIO2014, and EvoMusArt2014",

}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng

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