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Here we introduce RNAErnie, an RNA-focused pretrained model built upon the transformer architecture, employing two simple yet effective strategies. First, RNAErnie enhances pretraining by incorporating RNA motifs as biological priors and introducing motif-level random masking in addition to masked language modelling at base\/subsequence levels. It also tokenizes RNA types (for example, miRNA, lnRNA) as stop words, appending them to sequences during pretraining. Second, subject to out-of-distribution tasks with RNA sequences not seen during the pretraining phase, RNAErnie proposes a type-guided fine-tuning strategy that first predicts possible RNA types using an RNA sequence and then appends the predicted type to the tail of sequence to refine feature embedding in a post hoc way. Our extensive evaluation across seven datasets and five tasks demonstrates the superiority of RNAErnie in both supervised and unsupervised learning. It surpasses baselines with up to 1.8% higher accuracy in classification, 2.2% greater accuracy in interaction prediction and 3.3% improved F1 score in structure prediction, showcasing its robustness and adaptability with a unified pretrained foundation.<\/jats:p>","DOI":"10.1038\/s42256-024-00836-4","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T10:01:39Z","timestamp":1715594499000},"page":"548-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":108,"title":["Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3884-1820","authenticated-orcid":false,"given":"Ning","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[]},{"given":"Yuchen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xuhong","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6364-6149","authenticated-orcid":false,"given":"Shahid","family":"Mumtaz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9266-3044","authenticated-orcid":false,"given":"Linghe","family":"Kong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-3253","authenticated-orcid":false,"given":"Haoyi","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"836_CR1","doi-asserted-by":"publisher","first-page":"pdb","DOI":"10.1101\/pdb.top084970","volume":"2015","author":"K Kukurba","year":"2015","unstructured":"Kukurba, K. & Montgomery, S. 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