Social network analysis tools can identify disease biomarkers

The tools that are used to study social network data can also be employed to identify the biomarkers in our bodies for diseases, including different types of cancer, scientists say.
The advent of online social networks has led to the rapid development of tools for understanding the interactions between members of the network, their activity, and the connections, researchers said.
But, any relationships between lots of entities, whether users of Facebook and Twitter, bees in a colony, birds in a flock, or the genes and proteins in our bodies can be analysed with the same tools, researchers added.
In the new study Tansel Ozyer, Serkan Ucer and Taylan Iyidogan of the Department of Computer Engineering, at TOBB University, in Ankara, Turkey used the tools of Social Network Analysis (SNA) to understand and identify the biomarkers in our bodies for diseases.

SNA tools allow social network data to be displayed, manipulated and analysed graphically in a number of ways.
Researchers used the tools to unravel the connections and identify the biomarkers present in patient genomic microarray data.

By analogy with a social network, the team views genes as actors or members of the social network and similarities between different genes are considered to be the connections between these actors.
Genomic databases can be vast, given that the human genome comprises some 20,000 genes, and so such an approach can, the researchers suggest, dramatically decrease the number of features that must be analysed to find useful biomarkers.

Once identified and understood, such biomarkers can then be tested for in screening programmes for people at risk of a given disease or for diagnosis should the present with particular symptoms.
The team has demonstrated proof of principle with three types of cancer: lymphoma, colon cancer and leukemia.
"We showed how our approach is capable of effectively detecting cancer biomarkers out of high-dimensional genomic data," the team said.
"We combined clustering and classification into the developed framework to help in detecting the links between the various genes within the model and to validate the outcome, respectively," the researchers said.
The research is published in the International Journal of Data Mining and Bioinformatics.