A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{conf/evoW/AhmedZPX16,
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author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and
Bing Xue",
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title = "A Multi-objective Genetic Programming Biomarker
Detection Approach in Mass Spectrometry Data",
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booktitle = "19th European Conference on Applications of
Evolutionary Computation, EvoApplications 2016",
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year = "2016",
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editor = "Giovanni Squillero and Paolo Burelli",
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volume = "9597",
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series = "Lecture Notes in Computer Science",
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pages = "106--122",
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address = "Porto, Portugal",
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month = mar # " 30 -- " # apr # " 1",
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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bibdate = "2016-03-23",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#AhmedZPX16",
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isbn13 = "978-3-319-31204-0",
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DOI = "doi:10.1007/978-3-319-31204-0_8",
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abstract = "Mass spectrometry is currently the most commonly used
technology in biochemical research for proteomic
analysis. The main goal of proteomic profiling using
mass spectrometry is the classification of samples from
different clinical states. This requires the
identification of proteins or peptides (biomarkers)
that are expressed differentially between different
clinical states. However, due to the high
dimensionality of the data and the small number of
samples, classification of mass spectrometry data is a
challenging task. Therefore, an effective feature
manipulation algorithm either through feature selection
or construction is needed to enhance the classification
performance and at the same time minimise the number of
features. Most of the feature manipulation methods for
mass spectrometry data treat this problem as a single
objective task which focuses on improving the
classification performance. This paper presents two new
methods for biomarker detection through multi-objective
feature selection and feature construction. The results
show that the proposed multi-objective feature
selection method can obtain better subsets of features
than the single-objective algorithm and two traditional
multi-objective approaches for feature selection.
Moreover, the multi-objective feature construction
algorithm further improves the performance over the
multi-objective feature selection algorithm. This paper
is the first multi-objective genetic programming
approach for biomarker detection in mass spectrometry
data",
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notes = "EvoApplications2016 held inconjunction with
EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
- }
Genetic Programming entries for
Soha Ahmed
Mengjie Zhang
Lifeng Peng
Bing Xue
Citations