Title
Discovering cancer biomarkers: from DNA to communities of genes
Abstract
In this paper, we consider genes as actors of a social network, a research area that has not yet received attention in the literature of social network mining and analysis. Even though our research project covers both genes and proteins, we concentrate in this paper on gene; we first try to describe the gene expression data and how gene interactions can be realised as a social network. Then we describe how data mining techniques could reveal important information by identifying disease biomarkers from the social communities of genes. This is possible because of the way genes interact and form communities that are anticipated to have certain effects on the different processes that take place within an organism. Gene communities both contribute to the development of an organism by coding proteins and cause serious diseases. In this paper, we concentrate on genes that act as cancer biomarkers. We apply a multiobjective clustering approach to produce alternative clustering solutions and then derive a matrix that reflects the link between genes based on their common occurrence on the same cluster within different alternative solutions. The latter matrix leads to the social network of genes, which is then analysed to discover the communities and the central genes within each community. The latter genes are studied further as cancer biomarkers. The test results are promising in demonstrating the applicability and effectiveness of the developed mining-based methodology.
Year
DOI
Venue
2011
10.1504/IJNVO.2011.037166
IJNVO
Keywords
Field
DocType
networks,health,social networking,organisms,web mining,internet,social network analysis,data mining,social networks,world wide web,dna,clustering,deoxyribonucleic acid
Disease,Gene,Web mining,Social network,Computer science,Social network analysis,Knowledge management,Artificial intelligence,Cancer biomarkers,Computational biology,Cluster analysis,Organism
Journal
Volume
Issue
Citations 
8
1/2
0
PageRank 
References 
Authors
0.34
9
4
Name
Order
Citations
PageRank
Mohammed Al-shalalfa112410.26
Tansel Özyer219623.30
Reda Alhajj31919205.67
Jon G. Rokne426345.63