ParaMODA: Improving motif-centric subgraph pattern search in PPI networks

Mbadiwe, Somadina; Kim, Wooyoung

doi:10.1109/bibm.2017.8217920

Cited by 8 publications

(16 citation statements)

References 16 publications

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“…We propose NemoMap (Network Motif Mapping algorithm) as an extension of ParaMODA [1] which is an improvement of the motif-centric algorithm introduced by Grochow-Kellis (GK). We call it NemoMap since it is an improvement of the mapping function introduced by GK and we will include it in the NemoLib library [11].…”

Section: Nemomapmentioning

confidence: 99%

“…In FindSubgraphInstances in Algorithm 1, instead of going through all nodes h of H, we only arbitrarily choose one node h to traverse from. The chosen h node serves as the fixed starting point for all traversals of the query graph, and since the nodes of G are only traversed once with the chosen node h, the visited nodes g of G are not removed from the network [1]. There are no changes to IsomorphicExtensions in Algorithm 2.…”

Section: Paramodamentioning

confidence: 99%

“…The performance comparison will be discussed in Section 4. The logic behind the removal of the inner iteration comes from the fact that if the query graph is connected and the network is unchanged, any node of the network could be used as the starting point for mapping because the set of edges remain unchanged [1].…”

Section: Paramodamentioning

confidence: 99%

“…One of the biggest challenges in network analysis is to determine network motifs, defined as the subgraph patterns that occur more frequently in the original network than in similar randomized networks. As an extension of work originally presented in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017 [1], we provide an improved network motif detection algorithm in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we propose NemoMap (Network Motif Mapping algorithm) which is an extension of ParaMODA in [1]. NemoMap improves the performance of ParaMODA which is an improvement of the motifcentric algorithms developed by Grochow-Kellis (GK) [10].…”

Section: Introductionmentioning

confidence: 99%

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NemoMap: Improved Motif-centric Network Motif Discovery Algorithm

Huynh¹,

Mbadiwe²,

Kim³

2018

Adv. sci. technol. eng. syst. j.

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Network motif analysis has several applications in many different fields such as biological study and social network modeling, yet motif detection tools are still limited by the intensive computation. Currently, there are two categories for network motif detection method: network-centric and motif-centric approach. While most network-centric algorithms excel in enumerating all potential motifs of a given size, the runtime is infeasible for larger size of motifs. Researchers who are interested in larger motifs and have established a set of potential motif patterns could utilize motif-centric tools to check whether such patterns are truly network motifs by mapping them to the target network and counting their frequency. In the paper, we present NemoMap (Network Motif Mapping algorithm) which is an improvement of the motif-centric algorithm, GK (by Grochow and Kellis) and MODA (Motif Detection Algorithm). Experimental results on three different protein-protein interaction networks show that NemoMap is more efficient in mapping complex motif patterns, while GK and MODA is much faster in analyzing simpler patterns with fewer edges. We also compare the performance of NemoMap and ParaMODA (introduced previously to improve MODA), and the result shows that NemoMap yields better runtime due to the implementation of Grochow-Kellis' symmetry-breaking technique and the better node selection process.

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Section: Nemomapmentioning

confidence: 99%

Section: Paramodamentioning

confidence: 99%

Section: Paramodamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NemoMap: Improved Motif-centric Network Motif Discovery Algorithm

Huynh¹,

Mbadiwe²,

Kim³

2018

Adv. sci. technol. eng. syst. j.

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Review of tools and algorithms for network motif discovery in biological networks

Patra

Mohapatra

2020

IET syst. biol.

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Network motifs are recurrent and over‐represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)‐complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state‐of‐art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.

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NemoMapPy: Motif-centric network motif search on a web

Mar

Kim

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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ParaMODA: Improving motif-centric subgraph pattern search in PPI networks

Cited by 8 publications

References 16 publications

NemoMap: Improved Motif-centric Network Motif Discovery Algorithm

NemoMap: Improved Motif-centric Network Motif Discovery Algorithm

Review of tools and algorithms for network motif discovery in biological networks

NemoMapPy: Motif-centric network motif search on a web

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