Abstract
It can be difficult for biomedical researchers to understand complex molecular networks due to their unfamiliarity with the mathematical concepts employed. To represent molecular networks with clear meanings and familiar forms for biomedical researchers, we introduce a knowledge-based computational framework to decipher biomedical networks by making systematic comparisons to well-studied “basic networks”. A biomedical network is characterized as a spectrum-like vector called “network fingerprint”, which contains similarities to basic networks. This knowledge-based multidimensional characterization provides a more intuitive way to decipher molecular networks, especially for large-scale network comparisons and clustering analyses. As an example, we extracted network fingerprints of 44 disease networks in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The comparisons among the network fingerprints of disease networks revealed informative disease-disease and disease-signaling pathway associations, illustrating that the network fingerprinting framework will lead to new approaches for better understanding of biomedical networks.
Highlights
To compute the network fingerprint, we presented an algorithm to measure the functional and structural similarity between G and Pi based on the gene ontology (GO) and affinity propagation (AP) clustering algorithm[6], and the similarity score is normalized by the random simulation procedure
The disease networks were downloaded from the KEGG pathway database, which had been drawn manually based on current knowledge about these diseases
Based on our network fingerprint extracting algorithm, we obtained the fingerprints of these disease networks including 93 similarity scores compared to signaling pathways in the KEGG database (Fig. 1A)
Summary
There are already several methods to measure the similarity of two networks in computer science. These methods are almost based on topological structure of networks but ignore the network node property. The biological function of the proteins is an important factor when compare two biomedical networks. Our method takes both the topological structure and the function of the proteins in the network into consideration. Based on this approach, we provide new insights into the space of disease networks as well as the relationships between diseases and signaling pathways
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