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…
HyLnc combining transformer embeddings with handcrafted biological features improved lncRNA prediction to 91.3% accuracy on independent validation, outperforming existing tools.
Evidence
- Peer-reviewedRNA Biology2026-09-16
How should this claim be treated?
Truvace Impact Record TRV-2026-1122, v1: “HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.” Truvace, 2026-09-17. /record/TRV-2026-1122 (accessed at citation time). sha256 a62ee95113a4c276…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1122 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.