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CFPLncLoc:基于混沌游戏表示和集中功能金字塔的多标签 lncRNA 亚细胞局部化预测

Sheng Wang¹, Zu-Guo Yu¹, Guo-Sheng Han¹

¹National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China.

International journal of biological macromolecules

|January 6, 2025

概括

这项研究介绍了CFPLncLoc,这是一个深度学习模型,预测长非编码RNA (lncRNA) 细胞下定位. 它准确地识别了lncRNAs的多个位置,进步了对生物功能的理解.

科学领域:

分子生物学分子生物学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

长非编码RNAs (lncRNAs) 的细胞下定位对于理解它们的生物功能至关重要.
lncRNAs经常表现出多个亚细胞局部,这对现有的预测方法构成了挑战.
目前的计算工具在预测多标签 lncRNA 亚细胞定位方面是有限的.

研究的目的:

开发一种新的深度学习模型,CFPLncLoc,用于预测 lncRNAs 的多标签亚细胞定位.
为了利用IncRNA序列的混沌游戏表示 (CGR) 图像来提高预测准确性.
为了增强多标签预测任务的特征表示和提取.

主要方法:

使用混沌游戏表示 (CGR) 将 lncRNA 序列转换为图像.
实施图像更新策略 (IUS),以增强CGR图像中的特征表示.
从计算机视觉中集成了一个中心化的特征金字塔 (CFP) 模型,用于多尺度的特征融合.

主要成果:

废除研究表明,IUS和CFP都显著改善了预测性能.
在一个独立的测试组中,CFPLncLoc的表现优于现有的最先进的预测器.
该模型使用MaAUC评估指标取得了卓越的结果.

结论:

CFPLncLoc提供了一种有效的计算方法,用于预测lncRNAs的多标签亚细胞定位.
CGR,IUS和CFP的整合为lncRNA本地化预测提供了一个强大的框架.
该工具通过准确识别它们的亚细胞分布,推进了对lncRNA功能的研究.

关键词:

集中式特征金字塔是一个集中式特征金字塔.图像更新策略图像更新策略预测多标签的 lncRNA 亚细胞定位.统计测试统计测试混沌游戏的表现混乱游戏的表现

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