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Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 3 | Page No.: 459-468
DOI: 10.3923/jas.2009.459.468
Classification and Diagnostic Prediction of Cancers Using Gene Microarray Data Analysis
Alireza Osareh and Bita Shadgar

In this study, we aim to develop an automated system for robust and reliable cancer diagnoses based on gene microarray data. Amongst various utilized statistical classifiers, support vector machines outperform other popular classifiers, such as K nearest neighbours, naive Bayes, neural networks and decision tree, often to a remarkable degree. We choose a set of 9 publicly available benchmark microarray datasets that encompass both binary and multi-class cancer problems. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for use in gene-based cancer classification. In particular, amongst various systematic experiments carried out, best classification model is achieved using a subset of features chosen via information gain feature ranking for support vector machine classifier.
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  •    Differences in Gene Expression Profiles among the Proximal, Middle and Distal Peyer’s Patches in the Mouse Small Intestine
  •    Central Limit Theorem for the Sum of a Random Number of Dependent Random Variables
How to cite this article:

Alireza Osareh and Bita Shadgar, 2009. Classification and Diagnostic Prediction of Cancers Using Gene Microarray Data Analysis. Journal of Applied Sciences, 9: 459-468.

DOI: 10.3923/jas.2009.459.468








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