A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network

BMC Systems Biology

Hiroyuki Aburatani¹  Sigeo Ihara¹  Shingo Tsuji² 

[1] Genome Science Division, Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan;Komaba Open Laboratory, The University of Tokyo, Tokyo, Japan

关键词: Cancer research;    Omics data analysis;    Network data mining;    Knowledge-based analysis;   

Others  :  1143600
DOI  :  10.1186/1752-0509-6-124

 received in 2012-02-08, accepted in 2012-07-25,  发布年份 2012

【 摘 要 】

Background

In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes.

Results

To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes.

Conclusions

The developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.

【 授权许可】

   
2012 Tsuji et al.; licensee BioMed Central Ltd.

【 图 表 】

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