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Information Extraction from Microarray Data: A Survey of Data Mining Techniques

Information Extraction from Microarray Data: A Survey of Data Mining Techniques

Alessandro Fiori, Alberto Grand, Giulia Bruno, Francesco Gavino Brundu, Domenico Schioppa, Andrea Bertotti
Copyright: © 2016 |Pages: 32
ISBN13: 9781466695627|ISBN10: 1466695625|EISBN13: 9781466695634
DOI: 10.4018/978-1-4666-9562-7.ch060
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MLA

Fiori, Alessandro, et al. "Information Extraction from Microarray Data: A Survey of Data Mining Techniques." Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1180-1211. https://doi.org/10.4018/978-1-4666-9562-7.ch060

APA

Fiori, A., Grand, A., Bruno, G., Brundu, F. G., Schioppa, D., & Bertotti, A. (2016). Information Extraction from Microarray Data: A Survey of Data Mining Techniques. In I. Management Association (Ed.), Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1180-1211). IGI Global. https://doi.org/10.4018/978-1-4666-9562-7.ch060

Chicago

Fiori, Alessandro, et al. "Information Extraction from Microarray Data: A Survey of Data Mining Techniques." In Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1180-1211. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9562-7.ch060

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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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