基于多任务预测的图形对比学习,用于推断lncRNA,miRNA和疾病之间的关系
Nan Sheng1, Yan Wang1,2, Lan Huang1
1Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China.
Briefings in bioinformatics
|August 2, 2023
概括
这项研究引入了一种新的方法,用于预测长非编码RNA (lncRNA),microRNA (miRNA) 和疾病之间的关系. 该方法有效地识别了与疾病相关的lncRNA和miRNA,有助于疾病诊断和治疗.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 确定长非编码RNAs (lncRNAs),microRNAs (miRNAs) 和疾病之间的关系对于疾病诊断,治疗和预后至关重要.
- 现有的计算方法经常预测lncRNA-疾病关联 (LDA),miRNA-疾病关联 (MDA) 和lncRNA-miRNA相互作用 (LMI) 作为单独的任务,限制了信息互补性.
研究的目的:
- 开发一个统一的计算框架,用于LDA,MDA和LMI的同时多任务预测.
- 为了提高预测准确性,利用lncRNA,miRNA和疾病之间的相互信息.
主要方法:
- 提出一种新的无监督嵌入方法,用于多任务预测 (GCLMTP) 的图形对比学习.
- 构建一个三层 lncRNA-miRNA-疾病异质图 (LMDHG) 整合实体相似性和相关性.
- 使用图形对比学习与图形卷积网络来从LMDHG中提取拓特征.
主要成果:
- 在预测与疾病相关的lncRNAs和miRNAs方面,GCLMTP的性能优于最先进的方法.
- 案例研究表明,GCLMTP能够准确地发现新的lncRNA疾病和miRNA疾病关联.
- 该方法为LDA,MDA和LMI提供了准确的预测分数.
结论:
- GCLMTP提供了一个有效的统一框架,用于对lncRNA,miRNA和疾病关系的多任务预测.
- 无监督嵌入方法增强了复杂生物相互作用的发现.
- 公共可用的代码和数据集确保可重现性,并促进进一步的研究.
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