arXiv cs.AI· Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan·· 3 天前AI 评分18
基于 AI 的 RNA 亚细胞定位预测综述
A Comprehensive Review on RNA Subcellular Localization Prediction
AI 导读
这篇综述系统梳理了基于 AI/ML 的 RNA 亚细胞定位预测最新进展，覆盖 lncRNA、mRNA、miRNA 等多种 RNA 类型，聚焦基于序列、基于图像以及融合两类数据的混合方法。文章指出这类方法可加速 RNA 研究、揭示分子通路并指导靶向疾病治疗，同时讨论了数据稀缺、缺乏 benchmark 等挑战及应对机会。
正文 · 原文
Abstract:The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Genomics (q-bio.GN); Subcellular Processes (q-bio.SC) |
| Cite as: | arXiv:2504.17162 [cs.CV] |
| (or arXiv:2504.17162v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2504.17162 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1093/bfgp/elag011
DOI(s) linking to related resources |
Submission history
From: Cece Zhang [view email]
[v1]
Thu, 24 Apr 2025 00:47:31 UTC (578 KB)
[v2]
Sun, 11 Oct 2026 14:10:00 UTC (1,001 KB)
来源：arXiv cs.AI · arxiv.org