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BMC Systems Biology

, 5:S5

First Online: 23 December 2011

Abstract

BackgroundMolecular 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.

ResultsWe 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, EDGE GO-BNM, EDGE BETWEENNESS-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 EDGE GO-BNM and EDGE BETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well.

ConclusionWe provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

List of abbreviationsBNMBiological Network Motif

GOGene Ontology

BPBiological Process

MFMolecular Function

CCCellular Component

DAGDirected Acyclic Graph

SPShortest Path

NMFNon-negative Matrix Factorization

ERSExhaustive Recursive Search

ESUEnumerate SUbgraph

RAND-ESURandomized ESU.

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Autor: Wooyoung Kim - Min Li - Jianxin Wang - Yi Pan

Fuente: https://link.springer.com/







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