title: Current innovations and future challenges of network motif detection.
abstract: Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed.
authors: Ngoc Tam L
authors: NT
authors: Tran
authors: Sominder
authors: S
authors: Mohan
authors: Zhuoqing
authors: Z
authors: Xu
authors: Chun-Hsi
authors: CH
authors: Huang
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology; D005544:Forecasting; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods* / Q000639:trends; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome
keywords: graph isomorphism; network motif; network motif detection; random network; statistical significance