# α-HMM: A Graphical Model for RNA Folding

arXiv: https://arxiv.org/abs/2401.03571 · HTML: https://mlsanity.com/paper/2401.03571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [A Generalization of the Noisy-Or Model](https://mlsanity.com/paper/1303.1479/) (2013)
- [Review of Machine-Learning Methods for RNA Secondary Structure Prediction](https://mlsanity.com/paper/2009.08868/) (2020)

## 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.