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LOCAS:多标签mRNA本地化与监督对比学习

Abrar Rahman Abir¹, Md Toki Tahmid¹, M Saifur Rahman¹

¹Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh.

Briefings in bioinformatics

|August 27, 2025

概括

这项研究引入了LOCAS,一种用于预测信使RNA (mRNA) 亚细胞定位的新深度学习方法. LOCAS通过学习多个定位点之间的RNA序列表示和关系来提高准确性.

科学领域:

计算生物学
分子生物学
生物信息学

背景情况:

传递 RNA (mRNA) 的细胞下定位对于基因调节和蛋白质合成控制至关重要.
现有的计算方法经常使用单标签分类,未能解决mRNA本地化的多标签性质.
目前的深度学习方法难以模拟多个mRNA定位点之间的关系.

研究的目的:

开发一种新的计算框架,即监督对比学习的本地化 (LOCAS),用于准确的多标签mRNA亚细胞本地化预测.
使用监督对比学习增强多个本地化站点之间的关系建模.
通过更好的mRNA本地化预测,提高蛋白质合成的空间和时间控制的精度.

主要方法:

集成RNA语言模型 (RiNALMo) 来实现高质量的序列嵌入.
应用监督对比学习 (SCL) 来完善嵌入空间并确保具有生物意义的集群.
引入基于重叠值的相似度,以处理重叠的本地化标签.
使用具有交叉注意力机制的ML解码器来加强多标签分类.

主要成果:

在所有评估指标上,LOCAS在RNALocate和RNALocate V2.0基准数据集上取得了最先进的表现.
废除研究证实了对比学习和ML解码器对提高多标签分类准确性的重大贡献.
提出的方法有效地模拟了多个mRNA定位点之间的关系.

结论:

LOCAS为多标签mRNA细胞下定位预测提供了强大且可扩展的解决方案.
将RNA序列表示学习与SCL集成显著推进了mRNA本地化预测领域.
这些发现为通过精确的mRNA定位更精确地理解和控制基因表达铺平了道路.

关键词:

RNA语言模型对比式学习 mRNA亚细胞局部化

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