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α-HMM: A Graphical Model for RNA Folding

arXiv 2401.03571 ↗First posted 7 Jan 2024q-bio.BM cs.LGcited by 0

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Abstract (arXiv)

RNA secondary structure is modeled with the novel arbitrary-order hidden Markov model (α-HMM). The α-HMM extends over the traditional HMM with capability to model stochastic events that may be in influenced by historically distant ones, making it suitable to account for long-range canonical base pairings between nucleotides, which constitute the RNA secondary structure. Unlike previous heavy-weight extensions over HMM, the α-HMM has the flexibility to apply restrictions on how one event may influence another in stochastic processes, enabling efficient prediction of RNA secondary structure including pseudoknots.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 37 references that are in the map. First posted on arXiv 7 Jan 2024.

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References · 3

  1. 2020Explainable Deep Learning: A Field Guide for the Uninitiatedcited by 127
  2. 2013A Generalization of the Noisy-Or Modelcited by 5
  3. 2020Review of Machine-Learning Methods for RNA Secondary Structure Predictioncited by 3

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