ArticleScientific reports2025
Network motif detection using hidden markov models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- Network hypermotifs in biological systems: structure, dynamics, and functional implications.NPJ systems biology and applications · 2026Review
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2 authors.
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Abstract
Graphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs-or motifs-revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs) to network motif detection: subgraphs are encoded as short symbolic sequences and scored with standard HMM kernels (Viterbi/Forward; optional Baum-Welch), producing graded likelihoods that tolerate missing or noisy edges. On a 253-node directed benchmark the HMM pipeline recovers known 4-node motifs with accuracy comparable to exact enumeration while providing a probabilistic, weight-aware scoring framework that enables principled model comparison. We also include a concise complexity comparison with ESU, FANMOD and G-Tries and discuss engineering choices (seed-and-filter, scoring-only workflows) that make the approach practical. To our knowledge, this is the first application of HMMs to network motif detection.
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Registered trials
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