%0 Journal Article
	%A Frank Emmert Streib and  Matthias Dehmer and  Jing Liu and  Max Mühlhauser
	%D 2007
	%J International Journal of Medical and Health Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 8, 2007
	%T A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer
	%U https://publications.waset.org/pdf/436
	%V 8
	%X In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
	%P 495 - 500