Emmert-Streib, Frank ; Dehmer, Matthias ; Liu, Jing ; Mühlhäuser, Max (2007)
A systems approach to gene ranking from DNA microarray data of cervical cancer.
In: International Journal of Medical, Medicine and Health Sciences, 1 (8)
doi: 10.5281/zenodo.1328424
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Emmert-Streib, Frank ; Dehmer, Matthias ; Liu, Jing ; Mühlhäuser, Max |
| Art des Eintrags: | Bibliographie |
| Titel: | A systems approach to gene ranking from DNA microarray data of cervical cancer |
| Sprache: | Englisch |
| Publikationsjahr: | 22 August 2007 |
| Verlag: | World Academy of Science, Engineering and Technology |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Medical, Medicine and Health Sciences |
| Jahrgang/Volume einer Zeitschrift: | 1 |
| (Heft-)Nummer: | 8 |
| DOI: | 10.5281/zenodo.1328424 |
| URL / URN: | https://publications.waset.org/436/a-systems-approach-to-gen... |
| Kurzbeschreibung (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. |
| Zusätzliche Informationen: | 2005 ersch. in: Proceedings of the International Conference on Data Analysis, ICDA 2005, in conjunction with 6th International Enformatika Conference, Budapest/Hungary |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation |
| Hinterlegungsdatum: | 20 Nov 2008 08:23 |
| Letzte Änderung: | 22 Jul 2021 08:46 |
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