Abstract:
BACKGROUND:As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine. METHODS:In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles. RESULTS:We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers. CONCLUSION:Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Han Hdoi
10.1186/1471-2105-11-S1-S1subject
Has Abstractpub_date
2010-01-18 00:00:00pages
S1issn
1471-2105pii
1471-2105-11-S1-S1journal_volume
11 Suppl 1pub_type
杂志文章abstract:BACKGROUND:Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform...
journal_title:BMC bioinformatics
pub_type: 杂志文章
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journal_title:BMC bioinformatics
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journal_title:BMC bioinformatics
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pub_type: 杂志文章
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更新日期:2015-08-14 00:00:00
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更新日期:2014-02-26 00:00:00
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更新日期:2013-01-01 00:00:00
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更新日期:2004-10-28 00:00:00
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journal_title:BMC bioinformatics
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更新日期:2010-11-11 00:00:00
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更新日期:2018-11-19 00:00:00
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更新日期:2013-09-18 00:00:00
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更新日期:2018-05-30 00:00:00
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pub_type: 杂志文章
doi:10.1186/1471-2105-11-S8-S5
更新日期:2010-10-26 00:00:00
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更新日期:2017-03-14 00:00:00
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更新日期:2020-01-10 00:00:00
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更新日期:2014-07-21 00:00:00
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