相关实验视频
Updated: Jul 9, 2025

06:16
mirMachine: A One-Stop Shop for Plant miRNA Annotation
Published on: May 1, 2021
2.6K
基于强化超图卷积自编码器的miRNA疾病关联的预测
Guo-Bo Xie1, Jun-Rui Yu1, Zhi-Yi Lin1
1School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
Computational biology and chemistry
|December 6, 2023
概括
这项研究引入了一种新的方法,SHGAE,通过处理稀疏的数据来改善微RNA疾病关联预测. SHGAE增强了图形卷积网络,以更准确地预测潜在的微RNA疾病联系.
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
- 机器学习 机器学习
背景情况:
- 预测微RNA与疾病的关联对于理解疾病机制至关重要.
- 现有的图形神经网络方法与稀疏关联矩阵作斗争,限制了预测准确性.
研究的目的:
- 开发一种新的方法,强化超图卷积自编码器 (SHGAE),用于准确预测微RNA与疾病的关联.
- 克服现有模型中稀疏关联矩阵所带来的局限性.
主要方法:
- SHGAE利用强化超图神经网络 (SHGNN) 进行强大的节点嵌入.
- 一个强化的超图卷积网络模块 (SHGCN) 增强了图形关联,并减少了矩阵稀疏性.
- 基于注意力的融合和多层感知子解码器用于改进预测.
主要成果:
- 在多个指标上预测微RNA与疾病的关联方面,SHGAE显著超过了最先进的方法.
- 对结肠和肺部瘤的验证证明了SHGAE的预测能力.
- 该模型还显示了分析没有先前miRNA关联的胃瘤的有效性.
结论:
- SHGAE提供了一种强大而有效的方法来预测微RNA与疾病的关联,特别是在数据稀疏的场景中.
- 该方法能够处理高阶邻居嵌入和结合注意力机制的能力增强了其预测能力.
- SHGAE对推进精准医学和疾病研究具有前途.
相关概念视频
MicroRNAs
3.0K
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...
3.0K
lncRNA - Long Non-coding RNAs
8.6K
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...
8.6K
Genome-wide Association Studies-GWAS
13.5K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
13.5K

