Modeling RNA duplex dynamics with Gibbs sampling enhances base-pair prediction accuracy and reveals structural activity profiles
Bibliographic record
Abstract
The RNA secondary (2D) structure prediction problem consists in determining the set of base pairs that form within an RNA molecule from its sequence. A related task is the RNA hybridization problem, where two RNA strands interact to form a duplex. Thermodynamics-based methods typically rely on experimentally determined energy parameters to compute minimum free energy structures for both single-stranded RNAs and duplexes. Through the Boltzmann distribution, these parameters can be used to estimate base-pairing probabilities. Here, we leverage these probabilities to simulate RNA:RNA interaction dynamics. Inspired by the Ising model, we apply Gibbs sampling to model the stochastic formation and disruption of base pairs over time in RNA duplexes, ultimately deriving a consensus structure. The resulting method, MC-DuplexFold (mcdf), enhances base-pair prediction accuracy when integrated with other RNA 2D structure prediction algorithms. Through benchmarking, we reaffirm the previously observed trend that approximate or heuristic methods, such as RIsearch and Sfold, outperform exact methods like RNAcofold and DuplexFold in structural prediction accuracy. Additionally, mcdf provides structural activity statistics that can be incorporated into the modeling of miRNA primary transcripts, precursors, and target interactions, thereby refining predictions of miRNA:mRNA duplex dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".