[2603.22330] Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

[2603.22330] Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2603.22330: Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

Quantitative Biology > Biomolecules arXiv:2603.22330 (q-bio) [Submitted on 20 Mar 2026] Title:Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction Authors:Zhiyuan Chen, Zhenfeng Deng, Pan Deng, Yue Liao, Xiu Su, Peng Ye, Xihui Liu View a PDF of the paper titled Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction, by Zhiyuan Chen and 6 other authors View PDF HTML (experimental) Abstract:Accurate prediction of RNA secondary structure underpins transcriptome annotation, mechanistic analysis of non-coding RNAs, and RNA therapeutic design. Recent gains from deep learning and RNA foundation models are difficult to interpret because current benchmarks may overestimate generalization across RNA families. We present the Comprehensive Hierarchical Annotation of Non-coding RNA Groups (CHANRG), a benchmark of 170{,}083 structurally non-redundant RNAs curated from more than 10 million sequences in Rfam~15.0 using structure-aware deduplication, genome-aware split design and multiscale structural evaluation. Across 29 predictors, foundation-model methods achieved the highest held-out accuracy but lost most of that advantage out of distribution, whereas structured decoders and direct neural predictors remained markedly more robust. This gap persisted after controlling for sequence length and reflected both loss of structural coverage and incorrect higher-order wiring. Togethe...

Originally published on March 25, 2026. Curated by AI News.

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