Computational prediction models for cancer classification using mass spectrometry data
by Tuan D. Pham
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 2, No. 4, 2008

Abstract: Classification of human complex diseases such as cancers using high-throughput mass spectrometry data generated by modern proteomic technology has quickly become an attractive topic of research in bioinformatics. However, successful applications of such proteomic strategies for early disease detection are greatly dependent on the effectiveness of computational models for data analysis. Ultimately, the extraction of appropriate features that can represent the identities of different classes plays the frontal critical factor for any difficult classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large feature space. This paper addresses these two frontal issues for Mass Spectrometry (MS) classification. We apply two computational prediction models to extract features of MS data and then use vector quantisation to reduce the feature storage. We also introduce the technique of information fusion for classification enhancement. The proposed methodology was tested using an MS-based ovarian cancer dataset and the results were found to be superior to a support vector machine approach using a different feature for the same data.

Online publication date: Sun, 21-Dec-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com