Biological network motif detection and evaluation

Kim, Wooyoung; Li, Min; Wang, Jianxin; Pan, Yi

doi:10.1186/1752-0509-5-s3-s5

Cited by 50 publications

(24 citation statements)

References 29 publications

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“…The researcher started proposing different approaches and methods for finding motifs in a different biological network and improving performance and accuracy. Kim et al [10] use network motif to find the frequent patterns in the large biological network. As the size increases the complexity of problem increases.…”

Section: Related Workmentioning

confidence: 99%

Finding Frequent Patterns in Biological Networks

Rane¹,

Kaushik²

2018

IJET

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Frequent patterns help to discover the structural behavior of the complex biological network. The networks which show same global structure may have different local structure. The patterns are the actual fingerprints of the network. The pattern frequency and its significance carries very important functionality to classify and cluster the biological network. The sheer number of patterns get generated while traversing the network. Counting these patterns and determining their frequencies in the large biological network is ve ry challenging and computationally expensive task. Previously proposed methods are bound by the size of patterns and networks size. By using approximation method like sampling, we proposed an algorithm. The results show the proposed algorithm is faster than existing algorithms. Also, the error rate is minimum, which makes the proposed method more reliable.

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Section: Related Workmentioning

confidence: 99%

Finding Frequent Patterns in Biological Networks

Rane¹,

Kaushik²

2018

IJET

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“…A smaller but increasing body of work has tried to develop graph sampling methods that apply to motifs of size larger than three [24], [7], [25], [13], [9]. Applications in bioinformatics, in particular, have inspired these efforts due to the need for motif detection and motif-related computations in biology [26], [27]. There have been at least two classes of approaches in the estimation of the statistics of larger-size motifs: one based on edge sampling and the other based on random walks.…”

Section: B Related Workmentioning

confidence: 99%

Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in Large Graphs

Han

Sethu

2016

2016 IEEE 16th International Conference on Data Mining (ICDM)

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Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions on the full graph data. Methods to estimate the motif statistics based on random walks by sampling only a small fraction of the subgraphs in the large graph address both of these challenges. In this paper, we present a new algorithm, called the Waddling Random Walk (WRW), which estimates the concentration of motifs of any size. It derives its name from the fact that it sways a little to the left and to the right, thus also sampling nodes not directly on the path of the random walk. The WRW algorithm achieves its computational efficiency by not trying to enumerate subgraphs around the random walk but instead using a randomized protocol to sample subgraphs in the neighborhood of the nodes visited by the walk. In addition, WRW achieves significantly higher accuracy (measured by the closeness of its estimate to the correct value) and higher precision (measured by the low variance in its estimations) than the current state-of-the-art algorithms for mining subgraph statistics. We illustrate these advantages in speed, accuracy and precision using simulations on well-known and widely used graph datasets representing real networks. [13]. For example, the clustering coefficient (the number of triangles in relation to the number of wedges) has long served as an important metric in sociometry and social network analysis [14], [15]. In fact, the relative frequencies of network motifs are indicative of important properties of graphs such as modularity, the tendency of nodes in a network to form tightly interconnected communities, and even play a role in the organization and evolution of networks [6]. Knowledge of these motif statistics combined with homophily, the tendency of similar nodes to connect to one another, add to the ability of businesses such as Facebook to better mine their graphs and monetize their social platforms through targeted advertisements [16].Computing motif statistics, however, is rendered difficult by two challenges: one computational and the other having to do with restricted access to the full graph data. The computational challenge arises because accurate computation of the relative frequencies of different motifs requires enumeration of all the induced subgraphs and checking each for isomorphism to known motif types. The time complexity of enumerating all induced subgraphs of size k in a graph with V vertices and E edges is exponential in k with an upper bound of O(E k ) and a lower bound of O(V c k−1 ) [17]. Even when k is as small as 4, in a graph with only millions of edges, the number of motifs can reach hundreds of billions. The other problem is one of restricted access because the data on...

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“…However, despite attempts during the 20th century to introduce systems-level thinking into biology, notably with the work of Ludwig Von Bertalaffy [21,22], it is only in the early 2000’s with the development of high throughput molecular techniques, that systems biology has emerged as a significant field in biology. Attempts at modeling biological systems using existing mathematical tools revealed that, while some basic mathematical properties of networks could be ascribed biological relevance [23,24,25,26,27,28,29,30], novel analytic tools are needed in order to more aptly deal with the complexity of biological network architecture [31,32,33,34,35,36]. In this section, we will describe general network characteristics and their correlation to biological processes.…”

Section: General Concepts In Systems Biologymentioning

confidence: 99%

A Systems Biology Starter Kit for Arenaviruses

Droniou-Bonzom

Cannon

2012

Viruses

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Systems biology approaches in virology aim to integrate viral and host biological networks, and thus model the infection process. The growing availability of high-throughput “-omics” techniques and datasets, as well as the ever-increasing sophistication of in silico modeling tools, has resulted in a corresponding rise in the complexity of the analyses that can be performed. The present study seeks to review and organize published evidence regarding virus-host interactions for the arenaviruses, from alterations in the host proteome during infection, to reported protein-protein interactions. In this way, we hope to provide an overview of the interplay between arenaviruses and the host cell, and lay the foundations for complementing current arenavirus research with a systems-level approach.

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Biological network motif detection and evaluation

Cited by 50 publications

References 29 publications

Finding Frequent Patterns in Biological Networks

Finding Frequent Patterns in Biological Networks

Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in Large Graphs

A Systems Biology Starter Kit for Arenaviruses

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