FANMOD: a tool for fast network motif detection

By Sebastian Wernicke, Florian Rasche. 2006

790 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

The Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph Enumeration (2014, methodology): “Similarly, the FANMOD [3] tool was developed which, compared to Mfinder, improves performance by using the RAND-ESU algorithm [4].”
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, methodology): “As a side note, approximate counting algorithms are also widely used, especially for counting frequent network motifs [58], [59], [60], [61], and can provide a considerable”
Analysis of Biological Networks (2008, methodology): “Again, more detailed mathematical treatises on random network models are given elsewhere [1,17,50], here we outline the basic ideas only.”
The dynamics of animal social networks: analytical, conceptual, and theoretical advances (2014, neutral): “2004), MAVisto (Schreiber and Schwöbbermeyer 2005), and FANMOD (Wernicke and Rasche 2006).”
Learning the Structural Vocabulary of a Network (2017, methodology): “The second (DT11) is trained using 11 features: occurrence counts for each of the 11 four-node subgraph kernels in the graph (Wernicke & Rasche, 2006).”
Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types (2019, methodology): “Using a high-efficiency FANMOD algorithm for network motif detection [72], we detected three types of three-node FFLs: TF-FFLs, miRNA-FFLs, and composite-FFLs and formed the raw co-regulatory network.”
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse (2015, methodology): “Actually, using the network motif detection algorithm, FANDOM (49), we can identify all the three-node motifs ‘TF-miRNA-gene’ in the human and mouse regulatory networks, respectively.”
Hypergraphs and Cellular Networks (2009, methodology): “The latter is already implemented, e.g., in FANMOD [42], a motif-finding tool ready to deal with n-partite networks.”
Knowledge sharing and yahoo answers: everyone knows something (2008, methodology): “These profiles are constructed by counting the actual frequency of each triad in the QA network for that category, and then comparing that frequency against the expected frequency for randomized versions of the same network [13, 12, 21].”