α-HMM: A Graphical Model for RNA Folding
This work addresses RNA folding prediction for computational biology, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled RNA secondary structure prediction by introducing the α-HMM, a graphical model that extends traditional HMMs to handle long-range dependencies and pseudoknots, resulting in efficient predictions.
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.