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

  title={ParaMODA: Improving motif-centric subgraph pattern search in PPI networks},
  author={Somadina Mbadiwe and Wooyoung Kim},
  journal={2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  • Somadina Mbadiwe, Wooyoung Kim
  • Published 1 November 2017
  • Computer Science
  • 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Finding motifs in networks usually involves traversing through the network to enumerate all possible subgraphs of a given size, and then determining their statistical uniqueness by sampling subgraphs from many randomly generated networks that share similar features with the original network. Current algorithms for network motif analysis can be categorized into either network-centric or motif-centric algorithms. While network-centric algorithms cannot choose the subgraph patterns to search… 
NemoMapPy: Motif-centric network motif search on a web
  • Preston Mar, Wooyoung Kim
  • Computer Science
    2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • 2019
NemoMapPy is a fully web accessible motif-centric network motif discovery tool on a web and reduces the runtime of NemoMap by on average 70% in sub-graphs larger than 10 nodes.
Discovery of network motifs based on induced subgraphs using a dynamic expansion tree
The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks and avoids the unnecessary growth of patterns that do not have any statistical significance.
NemoMap: Improved Motif-centric Network Motif Discovery Algorithm
This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
NemoSuite: Web-based Network Motif Analytic Suite
Article history: Received: 27 August, 2020 Accepted: 19 December, 2020 Online: 25 December, 2020
Investigating statistical analysis for network motifs
  • Zican Li, Wooyoung Kim
  • Computer Science
  • 2021
Experimental results demonstrate that DIRECT is a good alternative to EXPLICIT in detection of small size of network motifs, because it is much faster than EXPLICITS in detection, and the results are generally consistent with those byEXPLICIT.


Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking
A novel algorithm for discovering large network motifs that achieves these goals, based on a novel symmetry-breaking technique, which eliminates repeated isomorphism testing, leading to an exponential speed-up over previous methods.
MODA: an efficient algorithm for network motif discovery in biological networks.
This paper presents a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently and is able to identifylarger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms.
Kavosh: a new algorithm for finding network motifs
A new algorithm, Kavosh, for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms, based on counting all k- size sub-graphs of a given graph (directed or undirected).
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
A novel algorithm is presented that allows estimation of subgraph concentrations and detection of network motifs at a runtime that is asymptotically independent of the network size and can be applied to estimate the concentrations of larger subgraphs in larger networks than was previously possible with exhaustive enumeration algorithms.
NetMODE: Network Motif Detection without Nauty
An implementation of a network motif detection package that can perform motif detection for node subgraphs when [Formula: see text], but does so without the use of Nauty, and includes a method for generating comparison graphs uniformly at random.
Biological network motif detection: principles and practice
The biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem are discussed.
Efficient Detection of Network Motifs
  • S. Wernicke
  • Computer Science, Medicine
    IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • 2006
Experiments on a testbed of biological networks show the new algorithms to be orders of magnitude faster than previous approaches, allowing for the detection of larger motifs in bigger networks than previously possible and thus facilitating deeper insight into the field.
Network Motif Detection: Algorithms, Parallel and Cloud Computing,and Related Tools
A survey of current algorithms for network motif detection and existing software tools, including existing parallel methods as well as cloud computing based search, and shows the promising potentials for the cloud Computing based motif search methods.
Current innovations and future challenges of network motif detection
This work analyzes 11 network motif detection tools and algorithms for efficiently detecting network motifs and discusses the challenges, future improvements, and future research directions.
MAVisto: a tool for the exploration of network motifs
UNLABELLED MAVisto is a tool for the exploration of motifs in biological networks. It provides a flexible motif search algorithm and different views for the analysis and visualization of network