A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer

Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muehlhaeuser

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationProceedings Of World Academy Of Science, Engineering And Technology, Vol 8
EditorsC Ardil
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
Publication typeA4 Article in conference proceedings
EventConference of the World-Academy-of-Science-Engineering-and-Technology - Budapest, Hungary
Duration: 26 Oct 200528 Oct 2005

Publication series

NameProceedings of World Academy of Science Engineering and Technology
ISSN (Print)1307-6884


ConferenceConference of the World-Academy-of-Science-Engineering-and-Technology


  • Graph similarity
  • DNA microarray data
  • cancer


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