Abstract
BackgroundA network motif is defined as a statistically significant and recurring subgraph pattern within a network. Most existing instance collection methods are not feasible due to high memory usage issues and provision of limited network motif information. They require a two-step process that requires network motif identification prior to instance collection. Due to the impracticality in obtaining motif instances, the significance of their contribution to problem solving is debated within the field of biology.ResultsThis paper presents NemoProfile, an efficient new network motif data model. NemoProfile simplifies instance collection by resolving memory overhead issues and is seamlessly generated, thus eliminating the need for costly two-step processing. Additionally, a case study was conducted to demonstrate the application of network motifs to existing problems in the field of biology.ConclusionNemoProfile comprises network motifs and their instances, thereby facilitating network motifs usage in real biological problems.
Highlights
A network motif is defined as a statistically significant and recurring subgraph pattern within a network
We provide a case study where NemoProfile is directly applied to the prediction of essential proteins from protein-protein interaction (PPI) networks
Networkcentric and motif-centric methods exist for finding motifs. While these methods have reduced computational costs, they have not overcome the prejudice towards network motifs in problem solving
Summary
A network motif is defined as a statistically significant and recurring subgraph pattern within a network. Most existing instance collection methods are not feasible due to high memory usage issues and provision of limited network motif information. They require a two-step process that requires network motif identification prior to instance collection. A network motif is defined as an overly frequent and unique subgraph pattern in a network, and it has been applied to solve various biological and medical problems: predicting protein-protein interactions [3], determining protein functions [4], detecting breast-cancer susceptibility genes [5], investigating for evolutionary conservation [6, 7], and discovering essential proteins [8, 9]. A broad spectrum of applications has been explored: ‘motif clustering’ [10], ‘motif themes’ [11], ‘relative graphlet frequency distances’ [12, 13], ‘motif modes’ [14], and ‘MotifScores’ [15]
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