aceHyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction
Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologica…
On independent validation, HyLnc, which pairs a BERT-style RNA model with handcrafted sequence features, reached 91.30% accuracy and 91.23% F1 in identifying lncRNAs, and was presented as outperforming several existing tools.
Evidence
- Peer-reviewedRNA Biology2026-09-16
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Truvace Impact Record TRV-2026-1122, v2: “HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.” Truvace, 2026-11-04. /record/TRV-2026-1122 (accessed at citation time). sha256 9086ca754fedbd08…
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