Differentiable RNA Secondary Structure Extraction for Deep Learning
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Computer Science > Machine Learning
arXiv:2609.30752 (cs)
[Submitted on 25 Sep 2026]
Title:Differentiable RNA Secondary Structure Extraction for Deep Learning
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Abstract:Many deep learning approaches to RNA secondary structure prediction have recently been proposed. They typically output a weight matrix $W$ where $W_{ij}$ is an arbitrary weight for base $i$ pairing with base $j$. Converting this matrix to a predicted secondary structure or base-pairing probability matrix typically involves ad hoc and problematic downstream algorithms. Despite the importance of this conversion step, which we refer to as structure extraction, it has received relatively little attention in the literature. In this work, we analyze how the congruence between training and extraction methods affects prediction performance. To do this, we compare four extraction algorithms: a Nussinov-like dynamic programming method, maximum-weight graph matching and the greedy extraction algorithms used by SPOT-RNA and RiNALMo. These are evaluated on outputs from the pretrained RiNALMo model and three toy models trained in this paper: a differentiable Nussinov-like model, a binary cross-entropy (BCE) baseline, and a model that incorporates a novel symmetric doubly stochastic matrix (SDSM) normalization algorithm during training which allows it to output base-pairing probability matrices directly, without a separate extraction step. This SDSM normalization algorithm is differentiable and can be added inline to any deep learning model during training and evaluation. We find that the performance of each extraction method depends strongly on how the corresponding model was trained. Considering the toy models themselves, the SDSM model showed the strongest overall performance: it outperformed the BCE baseline under all four extraction algorithms and produced pre-extraction outputs closest to the ground truth. These results suggest that SDSM normalization is a tractable alternative to traditional structure extraction.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.30752 [cs.LG] |
| (or arXiv:2609.30752v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30752
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Differentiable RNA Secondary Structure Extraction for Deep Learning, by Tyler Illman and 3 other authors
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