ArticleBriefings in bioinformatics2012
Biological network motif detection: principles and practice.
Article in Briefings in bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Network hypermotifs in biological systems: structure, dynamics, and functional implications.NPJ systems biology and applications · 2026Review
- Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues.Nature communications · 2025Article
- Transcriptomic signatures of prostate cancer progression: a comprehensive RNA-seq study.3 Biotech · 2025Article
- Article
- Community detection in empirical kinase networks identifies new potential members of signalling pathways.PLoS computational biology · 2023Article
- Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.IEEE transactions on visualization and computer graphics · 2023Article
- Emergence of dynamic properties in network hypermotifs.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Improve the product structural robustness based on network motifs in product development.Scientific reports · 2022Article
- Clustering and graph mining techniques for classification of complex structural variations in cancer genomes.Scientific reports · 2022Article
- Impact of second-order network motif on online social networks.The Journal of supercomputing · 2022Article
- GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases.BMC bioinformatics · 2021Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Computing Temporal Sequences Associated With Dynamic Patterns on theFrontiers in systems neuroscience · 2021Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- QTL mapping of adult plant and seedling resistance to leaf rust (Puccinia triticina Eriks.) in a multiparent advanced generation intercross (MAGIC) wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Understanding Metabolic Flux Behaviour in Whole-Cell Model Output.Frontiers in molecular biosciences · 2021Article
- Review of tools and algorithms for network motif discovery in biological networks.IET systems biology · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Network motifs are statistically overrepresented sub-structures (sub-graphs) in a network, and have been recognized as 'the simple building blocks of complex networks'. Study of biological network motifs may reveal answers to many important biological questions. The main difficulty in detecting larger network motifs in biological networks lies in the facts that the number of possible sub-graphs increases exponentially with the network or motif size (node counts, in general), and that no known polynomial-time algorithm exists in deciding if two graphs are topologically equivalent. This article discusses the biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem. A simple classification scheme is designed to analyze the strengths and weaknesses of several existing algorithms. Experimental results derived from a few comparative studies in the literature are discussed, with conclusions that lead to future research directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.