Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis

doi:10.1016/j.eswa.2011.03.025

Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis

2011-09-01

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DOI：10.1016/j.eswa.2011.03.025

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Gene expression profiling using DNA microarray technique has been shown as a promising tool to improve the diagnosis and treatment of cancer. Recently, many computational methods have been used to discover maker genes, make class prediction and class discovery based on gene expression data of cancer tissue. However, those techniques fall short on some critical areas. These included (a) interpretation of the solution and extracted knowledge. (b) Integrating various sources data and incorporating the prior knowledge into the system. (c) Giving a global understanding of biological complex systems by a complete knowledge discovery framework. This paper proposes a multiple-kernel SVM based data mining system. Multiple tasks, including feature selection, data fusion, class prediction, decision rule extraction, associated rule extraction and subclass discovery, are incorporated in an integrated framework. ALL-AML Leukemia dataset is used to demonstrate the performance of this system. (C) 2011 Elsevier Ltd. All rights reserved.

Keywords:

Support vector machine

Multiple-kernel learning

Feature selection

Data fusion

Decision rule

Associated rule

Subclass discovery

Gene expression

Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis

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