cncFinder:基于图形注意力网络的可解释学习模型,用于识别双功能长非编码RNA
Qiang Tang1, Yang Yu2, Min Shen1
1Key Laboratory of Non-coding RNA and Drug Discovery at Chengdu Medical College of Sichuan Province, School of Basic Medical Sciences, Chengdu Medical College, Chengdu 610500, China.
Molecular therapy. Nucleic acids
|January 26, 2026
概括
我们开发了cncFinder,这是一种新的AI工具,可以准确识别具有编码和非编码功能的双功能长非编码RNA (lncRNA). 这一进步有助于RNA生物学研究和潜在的治疗开发.
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.
- 基因组学就是基因组学.
背景情况:
- 长非编码RNA (lncRNAs) 具有双重的蛋白质编码和调控功能,称为双功能RNA.
- 准确识别双功能lncRNAs对于推进RNA生物学和生物标志物发现至关重要.
- 目前用于识别双功能lncRNA的方法需要提高准确性和范围.
研究的目的:
- 开发和验证一种新的计算模型,cncFinder,用于准确预测双功能lncRNAs.
- 提高对RNA多功能性及其对生物过程的影响的理解.
- 为研究人员提供一个用户友好的工具来识别双功能lncRNAs.
主要方法:
- 开发了cncFinder,这是一个基于图形注意力网络的模型,使用k-mer图形和Word2Vec进行特征编码.
- 采用图表注意网络来捕捉 lncRNA 转录中的复杂序列依赖性.
- 验证了cncFinder在独立测试数据集和跨物种数据 (老鼠,果) 上的性能.
主要成果:
- 与测试数据集的最先进模型相比,cncFinder表现出更好的预测性能.
- 该模型显示强度和广泛适用于不同物种.
- 解释性分析确定了生物相关的动机,包括起始密码子和Kozak类元素,验证了其生物相关性.
结论:
- cncFinder显著提高了双功能 lncRNA 预测的准确性和可解释性.
- 该工具为系统发现双功能lncRNA提供了强大的资源,为RNA多功能提供了新的见解.
- 一个用户友好的Web服务器提高了研究社区的可访问性.
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