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MVNMDA:一个多视图网络,结合语义和全球特征,用于预测miRNA-Disease关联

Chen Yang¹, Zhen Wang¹, Shanwen Zhang¹

¹School of Electronic Infomation, Xijing University, Xi'an 710123, China.

Molecules (Basel, Switzerland)

|January 11, 2024

概括

这项研究介绍了MVNMDA,一种用于预测微RNA与疾病关联的新型计算模型. MVNMDA集成了多视图功能以提高准确性,为疾病查和治疗开发提供了更有效的方法.

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:

微RNAs (miRNAs) 在人类疾病的发展和进展中至关重要.
确定miRNA与疾病的关联对于疾病查和治疗至关重要.
传统的miRNA疾病关联识别实验方法是繁的,耗时的,并且与大型数据集作斗争.

研究的目的:

开发一个高效的计算模型来预测潜在的miRNA-疾病关联.
克服依赖于不完整外部数据库的现有方法的局限性.
为了提高miRNA-疾病关联预测的准确性和适用性.

主要方法:

提出了一个多视图计算模型,MVNMDA,整合了miRNA和疾病特征的本地,全球和语义视图.
已知关联信息被用来构建初始节点特征.
建立了多个网络来提取低维特征嵌入.
一个级联注意力分类器被用于特征融合和精确的预测.

主要成果:

与现有的计算方法相比,MVNMDA在HMDD v2.0和HMDD v3.2数据集上表现出优异的性能.
广泛的实验验证了拟议的多视角方法的有效性.
案例研究证实了MVNMDA可靠的预测性能.

结论:

MVNMDA提供了一种有效和高效的计算方法,用于预测miRNA与疾病的关联.
该模型的多视图集成策略提高了预测准确度.
MVNMDA在推进疾病查和治疗策略方面具有重大潜力.

关键词:

注意力级联的注意力级联.一个元路径的元路径.微RNA疾病关联.多视图网络的多视图网络.

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相关概念视频

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...