File Entry: A Study of Network-based Approach for Cancer Classification

Created: 2012-05-11 01:49:20      Last updated: 2012-05-11 01:49:21
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Version created on: 2012-05-11 01:49:20


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 The advent of high-throughput techniques such as

microarray data enabled researchers to elucidate process in a
cell that fruitfully useful for pathological and medical. For
such opportunities, microarray gene expression data have been
explored and applied for various types of studies e.g. gene
association, gene classification and construction of gene
network. Unfortunately, since gene expression data naturally
have a few of samples and thousands of genes, this leads to a
biological and technical problems. Thus, the availability of
artificial intelligence techniques couples with statistical
methods can give promising results for addressing the
problems. These approaches derive two well known methods:
supervised and unsupervised. Whenever possible, these two
superior methods can work well in classification and clustering
in term of class discovery and class prediction. Significantly, in
this paper we will review the benefit of network-based in term
of interaction data for classification in identification of class
cancer.

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