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School of Information Technology and Electrical Engineering, The University of New South Wales, ADFA, Canberra, ACTAustralia. Please log in to your account. 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, data data dissertation mining ms proteomic seldi technique. 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.
Life and medical sciences. Health care information systems. Artificial intelligence. Computer vision. Computer vision problems. Interest point and salient region detections. Image manipulation. Learning paradigms. Supervised learning. Supervised learning by classification. Machine learning algorithms. Feature selection. Machine learning approaches. Classification and regression trees. Information systems applications. Data mining.
Check if you have access through your login credentials or your institution to get full access on this article. article Free Access. Computational prediction models for cancer classification using mass spectrometry data. Author: Tuan D. School of Information Technology and Electrical Engineering, The University of New South Wales, ADFA, Canberra, ACTAustralia View Profile. Published: 01 December Get Citation Alerts New Citation Alert added!
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Create a New Binder Name. Data data dissertation mining ms proteomic seldi technique Create. International Journal of Data Mining and Bioinformatics Volume 2, Issue 4. Previous Article Next Article. Index Terms. Applied computing. Computing methodologies. Computer graphics. Machine learning. Information systems. Login options Check if you have access through your login credentials or your institution to get full access on this article.
Sign in. Full Access Get this Article. Information Contributors Published in. ISSN: Inderscience Publishers Geneva 15, Switzerland. Publication History Online: 1 December Published: 1 December Author Tags pattern recognition cancer classification information fusion proteomic strategies ovarian cancer bioinformatics early detection feature extraction prediction models data mining early disease detection mass spectrometry.
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Seer's Nanoparticle Approach: A Novel Approach to Unbiased, Deep, Rapid and Scalable Proteomics
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