acc-Motif: Accelerated Network Motif Detection

Meira, Luís A. A.; Máximo, Vinícius R.; Fazenda, Álvaro Luiz; Conceição, Arlindo Flávio da

doi:10.1109/tcbb.2014.2321150

Cited by 35 publications

(18 citation statements)

References 10 publications

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“…Substantial research effort has been devoted to dealing with the algorithmic challenges of network motif identification and the related graph algorithms [17,22,1,28]. This has led to the development of a number of computational tools, including mfinder [12], MAVisto [25], NeMoFinder [6], FANMOD [29], Grochow-Kellis [9], Kavosh [11], MODA [30], NetMODE [14], Acc-MOTIF [18] and QuateXelero [13] (see also [28] for a review and detailed comparisons). Verification techniques have also been applied to study network motifs.…”

Section: Introductionmentioning

confidence: 99%

Formal Analysis of Network Motifs

Kugler

Dunn

Yordanov

2018

Preprint

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Abstract. A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

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Section: Introductionmentioning

confidence: 99%

Formal Analysis of Network Motifs

Kugler

Dunn

Yordanov

2018

Preprint

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“…It can be seen that with the size of motif grows, the time needed to identify it increases exponentially. Even more efficient network motif detection tools have been developed, the time complexity to detect four-nodes motifs in directed graphs is O(m 2 ), where m is the number of edges in the network 51 . Furthermore, after obtaining the motifs, it takes time to enumerate all cases for each motif pattern.…”

Section: Discussionmentioning

confidence: 99%

The exploration of network motifs as potential drug targets from post-translational regulatory networks

Zhang

Song

Bork

et al. 2016

Sci Rep

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Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

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“…Milo et al [20] introduced this method and launched MFINDER [21] in 2003. FANMOD [22] surpassed its predecessors in performance, becoming one of the standard tools in motif detection [23]. The network motif detection task has a huge computational cost.…”

Section: Definition 3 No-crossing Closed Quadmentioning

confidence: 99%

A Quad Motifs Index for Directed Link Prediction

Liu

Chang

et al. 2019

IEEE Access

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Network motif is a sub-graph pattern may reflect structure functions or reveal underneath mechanics. Some scholars have used triadic motifs for link prediction and achieve convincing results. But most of them ignore the effectiveness of quad motifs in calculating the similarity of two endpoints. In this article, a new method based on quad motifs is proposed. Two winner motifs are chosen among 199 sub-graphs. Local information of motifs is also taken into consideration. Experiments on nine real networks show that the index we proposed can improve prediction accuracy, compared with seven wellknown measures. INDEX TERMS Link prediction, quad motifs, directed networks.

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acc-Motif: Accelerated Network Motif Detection

Cited by 35 publications

References 10 publications

Formal Analysis of Network Motifs

Formal Analysis of Network Motifs

The exploration of network motifs as potential drug targets from post-translational regulatory networks

A Quad Motifs Index for Directed Link Prediction

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