病原RM:通过结合多视图特征,对致病性RNA甲基化位点进行计算推断
Hui Liu1, Jiani Ma1, Xianjun Ma1
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
PLoS computational biology
|November 10, 2025
概括
新的深度学习模型PathoRM识别了与疾病相关的致病性RNA甲基化部位. 这种计算工具有助于在表观转录层面上理解疾病机制.
科学领域:
- 经转录基因组学 (Epi-transcriptomics) 是一门关于经转录基因组学的学科.
- 计算生物学 计算生物学
- 基因组研究 基因组研究
背景情况:
- 鉴定致病性RNA甲基化位点对于疾病治疗至关重要.
- 试管体内方法在识别这些部位方面存在局限性,需要计算方法.
研究的目的:
- 开发PathoRM,一种生物知情深度学习模型,用于推断RNA甲基化部位与疾病之间的关联.
- 提供致病性RNA甲基化位点的准确和可解释的预测.
主要方法:
- 病理RM集成了RNA甲基化宿主序列和疾病描述.
- 它使用大型语言模型,多视图学习,图形神经网络,对抗训练和负采样.
- 一个注意力机制提高了生物解释性.
主要成果:
- 在各种指标和数据集中,PathoRM展示了准确而强大的预测性能.
- 该模型成功地提炼了语义上丰富的特征嵌入.
- 通过突出显示关键宿主序列元素来实现生物解释性.
结论:
- PathoRM提供了一种强大的计算工作流程,用于识别致病性RNA甲基化位点.
- 这种方法有助于在表观转录层揭示疾病病理.
- 预计该工具将通过促进发现致病性位点和保存的动机来推进基因组研究.
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