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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 (2005):
A systems approach to gene ranking from {DNA} Microarray Data of cervical cancer.
In: Proceedings of the International Conference on Data Analysis, ICDA 2005, in conjunction with 6th International Enformatika Conference, Budapest/Hungary, enformatica, [Conference or Workshop Item]

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: Conference or Workshop Item
Erschienen: 2005
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: German
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.

Title of Book: Proceedings of the International Conference on Data Analysis, ICDA 2005, in conjunction with 6th International Enformatika Conference, Budapest/Hungary
Publisher: enformatica
Uncontrolled Keywords: Graph similarity, DNA microarray data, cancer
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Date Deposited: 31 Dec 2016 12:59
Identification Number: EDLM:2005
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