aceImproving RNA secondary structure prediction through expanded training data
In recent years, deep learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, however, has lagged behind. Although several methods have shown moderate success in predicting RNA secondary and tertiary structures, none have reached the accuracy observed with contemporary protein models. The lack of success of these RNA structure prediction models has been proposed to be due to limited high-quality structural information that can be used as traini…
Retraining the SincFold deep-learning model on the expanded RNASSTR data set improved its de novo RNA secondary structure prediction on previously unseen RNA families.
Evidence
- Peer-reviewedRNA2026-10-16
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Truvace Impact Record TRV-2026-1980, v1: “Improving RNA secondary structure prediction through expanded training data.” Truvace, 2026-10-20. /record/TRV-2026-1980 (accessed at citation time). sha256 9c9c1733574a4cb8…
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