research-article

Investigating statistical analysis for network motifs

Authors:

Wooyoung KimAuthors Info & Claims

BCB '21: Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Article No.: 40, Pages 1 - 6

https://doi.org/10.1145/3459930.3469566

Published: 01 August 2021 Publication History

Abstract

Network motifs are frequent and statistically significant subgraph patterns in a network. Its statistical uniqueness is generally determined by an explicit generation of many random graphs followed by subgraph sampling, and computation of P-value or Z-score, which is called EXPLICIT. It absorbs most computational time in detection of network motifs as typically 1,000 number of random graphs are generated and analyzed. Here, we investigated a DIRECT method which was introduced as an alternative to EXPLICIT, to speed up the process by removing the need of the explicit generation of random graphs. Although DIRECT's efficiency was described in theory, it was never adapted to detection of network motifs in practice. Therefore, we investigated, implemented, and applied DIRECT with a different statistical measurement to determine network motifs. Experimental results demonstrate that DIRECT is a good alternative to EXPLICIT, because it is much faster than EXPLICIT in detection of small size of network motifs, and the results are generally consistent with those by EXPLICIT.

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Cited By

Ma YDea MCao LFukuda M(2024)Toward Implementing an Agent-based Distributed Graph Database System2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825052(3456-3465)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825052

Index Terms

Investigating statistical analysis for network motifs
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Biological networks

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Abstract
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cover image ACM Conferences

BCB '21: Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2021

603 pages

ISBN:9781450384506

DOI:10.1145/3459930

General Chairs:
Hongmei Jiang
Northwestern University
,
Xiuzhen Huang
Arkansas State University
,
Jiajie Zhang
The University of Texas Health Science Center at Houston

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SIGBIOM: ACM Special Interest Group on Biomedical Computing

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New York, NY, United States

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Published: 01 August 2021

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BCB '21

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SIGBIOM

BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 1 - 4, 2021

Florida, Gainesville

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Cited By

Ma YDea MCao LFukuda M(2024)Toward Implementing an Agent-based Distributed Graph Database System2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825052(3456-3465)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825052

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