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Data Mining Techniques for Microarray Datasets

Authors: Lei Liu; Jiong Yang 0001; Anthony K. H. Tung;

Data Mining Techniques for Microarray Datasets

Abstract

Data mining research, which focuses on scalable and effective knowledge discovery from databases, can provide timely solutions for the biologists in these aspects. In this article, we aim to provide platform in which various aspects of microarray data analysis is being introduced. We discuss in layman term how microarray datasets are generated and used in biological research. We use example from the real projects that we participate in to illustrate the potential of different technologies. We also discuss existing data mining tools and methods used for analyzing the microarray data sets and their biological implications. We also offer a wide range of analysis tools that can be applied to microarray gene expression analysis. Finally, we present a set of open problems and future research directions for microarray data analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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