Genetic Programming for Biomarker Detection in Mass Spectrometry Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{DBLP:conf/ausai/AhmedZP12,
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author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
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title = "Genetic Programming for Biomarker Detection in Mass
Spectrometry Data",
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booktitle = "25th Joint Conference Australasian Conference on
Artificial Intelligence, AI 2012",
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year = "2012",
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editor = "Michael Thielscher and Dongmo Zhang",
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volume = "7691",
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series = "Lecture Notes in Computer Science",
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pages = "266--278",
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address = "Sydney, Australia",
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month = dec # " 4-7",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-35100-6",
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DOI = "doi:10.1007/978-3-642-35101-3_23",
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abstract = "Classification of mass spectrometry (MS) data is an
essential step for biomarker detection which can help
in diagnosis and prognosis of diseases. However, due to
the high dimensionality and the small sample size,
classification of MS data is very challenging. The
process of biomarker detection can be referred to as
feature selection and classification in terms of
machine learning. Genetic programming (GP) has been
widely used for classification and feature selection,
but it has not been effectively applied to biomarker
detection in the MS data. In this study we develop a GP
based approach to feature selection, feature extraction
and classification of mass spectrometry data for
biomarker detection. In this approach, we firstly use
GP to reduce the redundant features by selecting a
small number of important features and constructing
high-level features, then we use GP to classify the
data based on selected features and constructed
features. This approach is examined and compared with
three well known machine learning methods namely
decision trees, naive Bayes and support vector machines
on two biomarker detection data sets. The results show
that the proposed GP method can effectively select a
small number of important features from thousands of
original features for these problems, the constructed
high-level features can further improve the
classification performance, and the GP method
outperforms the three existing methods, namely naive
Bayes, SVMs and J48, on these problems.",
- }
Genetic Programming entries for
Soha Ahmed
Mengjie Zhang
Lifeng Peng
Citations