基于剩余图形卷积网络与注意力机制的lncRNA和疾病关联的预测
Shengchang Wang1, Jiaqing Qiao1, Shou Feng2
1School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China.
Scientific reports
|March 2, 2024
概括
研究人员开发了ResGCN-A,这是一种使用注意力机制和残余图卷积网络的新方法,用于准确预测长非编码RNA (lncRNA) -疾病关联,改进了现有的生物信息学工具.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 长非编码RNAs (lncRNAs) 越来越多地被认为是它们在疾病发病过程中的角色.
- 识别 lncRNA 与疾病相关的传统方法往往是低效的.
- 生物信息学工具提高了研究这些关联的效率.
研究的目的:
- 提出一种新的预测方法,ResGCN-A,用于lncRNA与疾病的关联.
- 使用注意力机制提高 lncRNA-疾病关联预测的准确性.
- 整合不同的相似性测量,以增强特征提取.
主要方法:
- 综合的lncRNA和疾病相似性测量 (功能,高斯相互作用概况内核,语义).
- 使用剩余图形卷积网络 (ResGCN) 来进行局部特征提取.
- 使用注意力机制进行特征加权和融合,然后进行额外树木分类.
主要成果:
- 与其他五种方法相比,ResGCN-A实现了优异的预测性能.
- 实现了0.9916的曲线下面积 (AUC) 和0.9951.15的精度召回曲线下面积 (AUPR).
- 案例研究和稳定性评估证实了ResGCN-A.的有效性.
结论:
- ResGCN-A有效地融合了lncRNAs和疾病的本地和全球特征,以准确预测关联.
- 拟议的方法在预测 lncRNA-疾病关联方面取得了显著的改进.
- ResGCN-A为推进与lncRNA相关疾病的研究提供了有价值的工具.
相关概念视频
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
Protein Networks
3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Genome-wide Association Studies-GWAS
13.4K
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...
13.4K
Receiver Operating Characteristic Plot
167
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
167


