A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer
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.
[1] R. Bellman, Dynamic Programming. Princeton University Press, 1957
[2] M. Dehmer, Strukturelle Analyse web-basierter Dokumente, Ph.D Thesis,
Department of Computer Science, Technische Universit¨at Darmstadt,
2005
[3] E. W. Dijkstra, A note on two problems in connection with graphs.
Numerische Math., Vol. 1, 1959, 269-271
[4] F. Emmert-Streib., M. Dehmer, J. Kilian: Classification of large Graphs
by a local Tree decomposition, accepted to appear in: Proceedings of
DMIN-05, International Conference on Data Mining, in conjuction with
the 2005 World Congress in Applied Computing, Las Vegas/USA, 2005
[5] T. R. Golub et.al., Molecular Classification of Cancer: Class Discovery
and Class Prediction by Gene Expression Monitoring, Science, Vol. 286,
1999, 531-537
[6] F. Kaden, Graphmetriken und Distanzgraphen. ZKI-Informationen, Akad.
Wiss. DDR, Vol. 2 (82), 1982, 1-63
[7] F. Kaden, Graph metrics and distance-graphs. In: Graphs and other
Combinatorial Topics, ed. M. Fiedler, Teubner Texte zur Math., Leipzig,
Vol. 59, 1983, 145-158
[8] P. J. Kraulis, Molscript: A Program to Produce Both detailed and
schematic plots of protein structures. Journal of Applied Crystallography,
Vol. 24, 1991, 946-950
[9] K.Mori et al., Highly specific marker genes for detecting minimal gastric
cancer cells in cytology negative peritoneal washings, Biochem. Biophys.
Res. Commun. 23;313(4):931-937 (2004).
[10] V. Batagelj and A. Mrvar, Pajek - Program for Large Network Analysis,
Connections 21:47-57 (1998).
[11] R. C. Read and D. G. Corneil, The graph isomorphism disease. Journal
of Graph Theory, Vol. 1, 1977, 339-363
[12] J. Rougemont and P. Hingamp, DNA microarray data and contextual
analysis of correlation graphs. BMC Bioinformatics, Vol. 4, 2003, 4-15
[13] F. Sobik, Graphmetriken und Klassifikation strukturierter Objekte. ZKIInformationen,
Akad. Wiss. DDR, Vol. 2 (82), 1982, 63-122
[14] F. Sobik, Graphmetriken und Charakterisierung von Graphklassen. 27.
Internat. Wiss. Koll., TH-Ilmenau, Vol. 2 (82), 1982, 63-122
[15] J. R. Ullman, An algorithm for subgraph isomorphism. J. ACM, Vol. 23
(1), 1976, 31-42
[16] Y. Wang et al., Gene expression profiles and molecular markers to
predict recurrence of Dukes-B colon cancer, J. Clin. Oncol. 1;22(9):1564-
1571 (2004).
[17] Y. F. Wong et.al. Expression Genomics of Cervical Cancer: Molecular
Classification and Prediction of Radiotherapy Response by DNA Microarray.
Clinical Cancer Research, Vol. 9, 2003, 5486-5492
[18] B. Zelinka, On a certain distance between isomorphism classes of
graphs. ˇ Casopis pro ˇpest. Mathematiky, Vol. 100, 1975, 371-373
[1] R. Bellman, Dynamic Programming. Princeton University Press, 1957
[2] M. Dehmer, Strukturelle Analyse web-basierter Dokumente, Ph.D Thesis,
Department of Computer Science, Technische Universit¨at Darmstadt,
2005
[3] E. W. Dijkstra, A note on two problems in connection with graphs.
Numerische Math., Vol. 1, 1959, 269-271
[4] F. Emmert-Streib., M. Dehmer, J. Kilian: Classification of large Graphs
by a local Tree decomposition, accepted to appear in: Proceedings of
DMIN-05, International Conference on Data Mining, in conjuction with
the 2005 World Congress in Applied Computing, Las Vegas/USA, 2005
[5] T. R. Golub et.al., Molecular Classification of Cancer: Class Discovery
and Class Prediction by Gene Expression Monitoring, Science, Vol. 286,
1999, 531-537
[6] F. Kaden, Graphmetriken und Distanzgraphen. ZKI-Informationen, Akad.
Wiss. DDR, Vol. 2 (82), 1982, 1-63
[7] F. Kaden, Graph metrics and distance-graphs. In: Graphs and other
Combinatorial Topics, ed. M. Fiedler, Teubner Texte zur Math., Leipzig,
Vol. 59, 1983, 145-158
[8] P. J. Kraulis, Molscript: A Program to Produce Both detailed and
schematic plots of protein structures. Journal of Applied Crystallography,
Vol. 24, 1991, 946-950
[9] K.Mori et al., Highly specific marker genes for detecting minimal gastric
cancer cells in cytology negative peritoneal washings, Biochem. Biophys.
Res. Commun. 23;313(4):931-937 (2004).
[10] V. Batagelj and A. Mrvar, Pajek - Program for Large Network Analysis,
Connections 21:47-57 (1998).
[11] R. C. Read and D. G. Corneil, The graph isomorphism disease. Journal
of Graph Theory, Vol. 1, 1977, 339-363
[12] J. Rougemont and P. Hingamp, DNA microarray data and contextual
analysis of correlation graphs. BMC Bioinformatics, Vol. 4, 2003, 4-15
[13] F. Sobik, Graphmetriken und Klassifikation strukturierter Objekte. ZKIInformationen,
Akad. Wiss. DDR, Vol. 2 (82), 1982, 63-122
[14] F. Sobik, Graphmetriken und Charakterisierung von Graphklassen. 27.
Internat. Wiss. Koll., TH-Ilmenau, Vol. 2 (82), 1982, 63-122
[15] J. R. Ullman, An algorithm for subgraph isomorphism. J. ACM, Vol. 23
(1), 1976, 31-42
[16] Y. Wang et al., Gene expression profiles and molecular markers to
predict recurrence of Dukes-B colon cancer, J. Clin. Oncol. 1;22(9):1564-
1571 (2004).
[17] Y. F. Wong et.al. Expression Genomics of Cervical Cancer: Molecular
Classification and Prediction of Radiotherapy Response by DNA Microarray.
Clinical Cancer Research, Vol. 9, 2003, 5486-5492
[18] B. Zelinka, On a certain distance between isomorphism classes of
graphs. ˇ Casopis pro ˇpest. Mathematiky, Vol. 100, 1975, 371-373
@article{"International Journal of Medical, Medicine and Health Sciences:49575", author = "Frank Emmert Streib and Matthias Dehmer and Jing Liu and Max Mühlhauser", title = "A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer", 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.", keywords = "Graph similarity, DNA microarray data, cancer.", volume = "1", number = "8", pages = "466-6", }