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Biological Network Motif Detection and Evaluation by Wooyoung Kim; Min Li; Jianxin Wang; Yi Pan is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Biological Network Motif Detection and Evaluation about?

## Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. ## Results We define __biological network motifs__ as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including __motifs included in complex, motifs included in functional module__ and __GO term clustering score__ in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. ## Concl

Who reads Biological Network Motif Detection and Evaluation?

Author

Wooyoung Kim; Min Li; Jianxin Wang; Yi Pan

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, [2006]-; Springer Science and Business Media LLC (ISSN 1752-0509)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)