在属性和拓图形上进行自我监督的对比学习,用于预测 lncRNA,miRNA和疾病之间的关系
IEEE journal of biomedical and health informatics
|September 24, 2024
概括
这项研究介绍了SSCLMD,一种新型的计算模型,使用自我监督的对比学习来预测长非编码RNA (lncRNA) -疾病关联 (LDA),miRNA-疾病关联 (MDA) 和lncRNA-miRNA相互作用 (LMIs). 该模型有效地整合了多任务学习和领域知识,以提高准确性.
科学领域:
- 计算生物学 计算生物学
- 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:
- 识别长非编码RNAs (lncRNAs),microRNAs (miRNAs) 和疾病之间的关联对于医学进步至关重要.
- 这些关系的实验验证是昂贵和耗时的,需要高效的计算方法.
- 现有的计算方法往往缺乏多任务能力,并为复杂的生物分子相互作用而努力使用有限的标记数据.
研究的目的:
- 开发一个多任务预测模型,SSCLMD,用于识别潜在的长非编码RNA疾病关联 (LDA),miRNA疾病关联 (MDA) 和lncRNA-miRNA相互作用 (LMIs).
- 利用属性和拓图形上的自主监督对比学习来克服数据稀缺的挑战.
- 通过整合域知识和多视图功能融合,增强预测模型的概括能力.
主要方法:
- 构建属性和拓图形,整合有关lncRNAs,miRNAs,疾病及其相互作用的领域知识.
- 在属性和拓空间中嵌入节点,使用注意力机制来适应多视图特征的融合.
- 应用对比的自我监督学习作为调节器来引导节点嵌入而不依赖标签,增强多任务学习.
主要成果:
- 在两个精心策划的数据集中,SSCLMD在预测LDA,MDA和LMI方面明显优于现有的基线方法.
- 该模型在多任务预测方面表现强,有效地利用集成的域知识和交互数据.
- 案例研究证实了SSCLMD在识别新型疾病相关的lncRNA和miRNA方面的有效性,验证了其预测能力.
结论:
- SSCLMD提供了一个强大而准确的计算框架,用于预测与疾病相关的复杂生物分子关联.
- 自主监督的对比学习和多任务学习的整合有效地解决了数据稀缺问题,并改善了模型的概括性.
- 这种方法为加速在疾病研究中发现新生物标志物和治疗点提供了有价值的工具.
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