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Open Access Highly Accessed Open Badges Methodology article

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

Shingo Tsuji12, Sigeo Ihara1 and Hiroyuki Aburatani1*

Author Affiliations

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

2 Komaba Open Laboratory, The University of Tokyo, Tokyo, Japan

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BMC Systems Biology 2012, 6:124  doi:10.1186/1752-0509-6-124

Published: 15 September 2012



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.


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.


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.

Knowledge-based analysis; Network data mining; Omics data analysis; Cancer research