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[2507.16801] Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models
Summary
This paper presents UTR-STCNet, a novel deep learning model designed to analyze 5' untranslated regions (5'UTRs) for improved prediction of mRNA translation efficiency, enhancing interpretability and flexibility in biological applications.
Why It Matters
Understanding the role of 5'UTRs in mRNA translation is crucial for advancements in genetic therapies and protein expression control. This research introduces a more effective model that not only improves predictive accuracy but also offers insights into the underlying mechanisms of translation regulation, which could lead to better therapeutic strategies.
Key Takeaways
- UTR-STCNet utilizes a Transformer-based architecture for flexible modeling of 5'UTRs.
- The model integrates a Saliency-Aware Token Clustering module for improved interpretability.
- It outperforms existing models in predicting mean ribosome load, a key indicator of translational efficiency.
- The architecture recovers known functional elements, providing mechanistic insights into translation regulation.
- This research could enhance the design of therapeutic mRNAs.
Quantitative Biology > Quantitative Methods arXiv:2507.16801 (q-bio) [Submitted on 22 Jul 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models Authors:Yuxi Lin, Yaxue Fang, Zehong Zhang, Zhouwu Liu, Siyun Zhong, Zhongfang Wang, Fulong Yu View a PDF of the paper titled Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models, by Yuxi Lin and Yaxue Fang and Zehong Zhang and Zhouwu Liu and Siyun Zhong and Zhongfang Wang and Fulong Yu View PDF HTML (experimental) Abstract:Understanding how 5' untranslated regions (5'UTRs) regulate mRNA translation is critical for controlling protein expression and designing effective therapeutic mRNAs. While recent deep learning models have shown promise in predicting translational efficiency from 5'UTR sequences, most are constrained by fixed input lengths and limited interpretability. We introduce UTR-STCNet, a Transformer-based architecture for flexible and biologically grounded modeling of variable-length 5'UTRs. UTR-STCNet integrates a Saliency-Aware Token Clustering (SATC) module that iteratively aggregates nucleotide tokens into multi-scale, semantically meaningful units based on saliency scores. A Saliency-Guided Transformer (SGT) block then captures both local and distal regulatory dependencies using a lightweight attention mechanism. This combined architecture achieves efficient and...