通过基于灵敏度的图形减少的图形卷积网络对传染病药物的重新定位.
Rongting Yue1, Abhishek Dutta2
1Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, USA. rongting.yue@uconn.edu.
Interdisciplinary sciences, computational life sciences
|December 4, 2024
概括
这项研究使用计算系统生物学和图形卷积网络 (GCNs) 快速识别潜在的药物重新定位候选人,以应对寨卡和COVID-19等新兴传染病.
科学领域:
- 计算系统生物学计算系统生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 新出现的传染病需要快速的治疗开发.
- 计算方法对于通过重新定位来识别候选药物至关重要.
- 现有的方法需要提高速度和准确性.
研究的目的:
- 开发一个计算管道,以快速重新利用药物来对抗新出现的传染病.
- 使用图形卷积网络 (GCN) 和灵敏度分析来提高预测性能.
- 为了确定寨卡病毒和COVID-19的新药候选者.
主要方法:
- 使用Kronecker产品开发了用于灵敏度分析的新分析表达式.
- 实现基于灵敏度的图形缩小,以改进模型.
- 整合RNA-seq数据,分子相互作用和GCN来构建异质图.
- 将管道应用于寨卡病毒和COVID-19数据集.
主要成果:
- 确定了与寨卡病毒和COVID-19有关的疾病相关的基因和途径.
- 成功预测了潜在的候选药物,包括贝他酸和比塞莱辛治疗寨卡病毒,以及诺基因,肝素二糖化物和Resveratrol治疗COVID-19.
- 通过文献审查和对接分析验证候选药物.
- 展示了一个可扩展和具有成本效益的计算药物重定向管道.
结论:
- 拟议的计算方法显著提高了药物重定向效率,以满足紧急医疗保健需求.
- 基于灵敏度的图形减小可以提高GCN模型的预测准确性.
- 这种方法提供了一种有前途的策略,通过快速药物发现来对抗新兴传染病.
相关概念视频
01:10Retrovirus Life Cycles
45.6K
Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
45.6K
02:26Protein 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