TY - GEN
T1 - A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer
AU - Emmert-Streib, Frank
AU - Dehmer, Matthias
AU - Liu, Jing
AU - Muehlhaeuser, Max
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Graph similarity
KW - DNA microarray data
KW - cancer
KW - MOLECULAR CLASSIFICATION
KW - PREDICTION
M3 - Conference contribution
T3 - Proceedings of World Academy of Science Engineering and Technology
SP - 82
EP - 87
BT - Proceedings Of World Academy Of Science, Engineering And Technology, Vol 8
A2 - Ardil, C
PB - WORLD ACAD SCI, ENG & TECH-WASET
T2 - Conference of the World-Academy-of-Science-Engineering-and-Technology
Y2 - 26 October 2005 through 28 October 2005
ER -
