JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 1    Issue : 5    Date : August 2006

Cancer Classification With MicroRNA Expression Patterns Found By An Information Theory Approach
Yun Zheng and Chee Keong Kwoh
Page(s): 30-39
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Abstract
Some non-coding small RNAs, known as microRNAs (miRNAs), have been shown to play important
roles in gene regulation and various biological processes. The abnormal expression of some specific
miRNA genes often results in the development of cancer. In this paper, we find discriminatory miRNA
patterns for cancer classification from miRNA expression profiles with an information theory approach.
Our approach evaluates subset of miRNAs by checking the mutual information between these miRNAs
and the class attribute I(X; Y ) with respect to the entropy of the class attribute H(Y ). Then, optimal
subset of miRNAs that satisfies I(X; Y ) = H(Y ) or H(Y ) − I(X; Y ) ≤  × H(Y ) for noisy data sets are
chosen to build the classification models. The experimental results show that the expression patterns
from a small set of miRNAs are very accurate in prediction. Further, the experimental results also
suggest that the expression patterns of these informative miRNAs are conserved in different vertebrates
during the evolution process.

Index Terms
microRNAs, gene expression patterns, cancer classification, feature selection