A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer
Joint Authors
Wu, Jiang
Ji, Yanju
Zhao, Ling
Ji, Mengying
Ye, Zhuang
Li, Suyi
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-23
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Background.
Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer.
However, the raw MS data is highly dimensional and redundant.
Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.
Methods.
The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples.
An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set.
Additionally, by the same data set, we also established a traditional PCA-SVM model.
Finally we compared the two models in detection accuracy, specificity, and sensitivity.
Results.
Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively.
In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.
Conclusions.
The PPCA-SVM model had better detection performance.
And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.
American Psychological Association (APA)
Wu, Jiang& Ji, Yanju& Zhao, Ling& Ji, Mengying& Ye, Zhuang& Li, Suyi. 2016. A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1100158
Modern Language Association (MLA)
Wu, Jiang…[et al.]. A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-6.
https://search.emarefa.net/detail/BIM-1100158
American Medical Association (AMA)
Wu, Jiang& Ji, Yanju& Zhao, Ling& Ji, Mengying& Ye, Zhuang& Li, Suyi. A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1100158
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100158