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Information Extraction from Microarray Data: A Survey of Data Mining Techniques
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Author(s): Alessandro Fiori (Institute for Cancer Research and Tretment, Italy), Alberto Grand (Institute for Cancer Research and Tretment, Italy), Giulia Bruno (Politecnico di Torino, Italy), Francesco Gavino Brundu (Politecnico di Torino, Italy), Domenico Schioppa (University of Torino Medical School, Italy)and Andrea Bertotti (University of Torino Medical School, Italy)
Copyright: 2016
Pages: 32
Source title:
Business Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-9562-7.ch060
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outcome based on genetic profiles, and inferring regulatory networks. This paper discusses popular and recent data mining techniques (i.e., Feature Selection, Clustering, Classification, and Association Rule Mining) applied to microarray data. The main characteristics of microarray data and preprocessing procedures are presented to understand the critical issues introduced by gene expression values analysis. Each technique is analyzed, and relevant examples of pertinent literature are reported. Moreover, real use cases exploiting analytic pipelines that use these methods are also introduced. Finally, future directions of data mining research on microarray data are envisioned.
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