跨多个异质网络的互补特征学习和多模式属性学习,用于预测与疾病相关的miRNAs
Ping Xuan1,2, Jinshan Xiu1, Hui Cui3
1School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
iScience
|February 2, 2024
概括
我们开发了CMMDA,一种新的计算方法,通过整合复杂的网络数据来识别与疾病相关的microRNA (miRNA). 这种方法提高了对疾病机制的理解,并有助于发现潜在的治疗点.
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:
- 识别与疾病相关的microRNAs (miRNAs) 对于理解疾病的发病过程至关重要.
- 现有的方法往往难以有效地整合各种数据类型和网络环境.
研究的目的:
- 提出CMMDA,一种推断潜在疾病相关miRNAs的计算方法.
- 有效编码和整合来自异质网络的上下文关系,互补信息和多模式属性.
主要方法:
- 基于疾病相似性构建多个异质网络.
- 使用基于变压器的特征表示用于miRNA和疾病节点.
- 采用了共同注意力融合机制来实现网络间信息集成.
- 开发了一种深度可分离的卷积模块,用于多模式属性编码.
主要成果:
- 与现有方法相比,CMMDA在推断与疾病相关的miRNA方面表现优越.
- 废弃性研究证实了CMMDA框架内核心创新的有效性.
结论:
- CMMDA为miRNA-疾病关联推断提供了一种强大的方法.
- 该方法能够整合多网络环境和多模式属性,从而推动生物信息学领域的发展.
相关概念视频
01:22MicroRNAs
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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...
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02:39lncRNA - Long Non-coding RNAs
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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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