TU Darmstadt / ULB / TUbiblio

A systems approach to gene ranking from DNA microarray data of cervical cancer

Emmert-Streib, Frank and Dehmer, Matthias and Liu, Jing and Mühlhäuser, Max (2007):
A systems approach to gene ranking from DNA microarray data of cervical cancer.
In: International Journal of Medical and Health Sciences, 1 (8), pp. 495-500. World Academy of Science, Engineering and Technology, DOI: 10.5281/zenodo.1328424,
[Article]

Abstract

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.

Item Type: Article
Erschienen: 2007
Creators: Emmert-Streib, Frank and Dehmer, Matthias and Liu, Jing and Mühlhäuser, Max
Title: A systems approach to gene ranking from DNA microarray data of cervical cancer
Language: English
Abstract:

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.

Journal or Publication Title: International Journal of Medical and Health Sciences
Journal volume: 1
Number: 8
Publisher: World Academy of Science, Engineering and Technology
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Date Deposited: 20 Nov 2008 08:23
DOI: 10.5281/zenodo.1328424
Official URL: https://publications.waset.org/436/a-systems-approach-to-gen...
Additional Information:

2005 ersch. in: Proceedings of the International Conference on Data Analysis, ICDA 2005, in conjunction with 6th International Enformatika Conference, Budapest/Hungary

License: [undefiniert]
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details