基于网络的多主题药物发现中的综合分析方法:系统性审查.
Wei Jiang1, Weicai Ye2, Xiaoming Tan1
1School of Life Sciences, Hubei University, Wuhan, China.
BioData mining
|March 29, 2025
概括
基于网络的多omics集成加速了药物发现. 本综述对方法进行了分类,并评估了它们在目标识别,响应预测和重新定位方面的使用,强调了挑战和未来方向.
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
- 药理学 药理学是指药理学的学科.
背景情况:
- 多omics数据集成对于现代药物发现至关重要.
- 基于网络的方法为分析复杂的生物数据提供了强大的工具.
- 缺乏在药物发现背景下对这些方法的系统评估.
研究的目的:
- 系统地审查和分类基于网络的多omics集成方法.
- 评估这些方法在药物发现关键阶段的应用.
- 确定该领域的挑战和未来的研究方向.
主要方法:
- 在药物发现中基于网络的多omics集成的综合文献综述 (2015-2024年).
- 将方法分类为网络传播/扩散,基于相似性,图形神经网络和网络推理.
- 在药物标识,反应预测和药物重定位方面的应用分析.
主要成果:
- 确定了基于网络的多领域集成方法的四大类.
- 在药物标识,响应预测和重新定位方面展示了应用.
- 突出了不同方法在特定药物发现任务中的优点和局限性.
结论:
- 基于网络的多omics集成显示了促进药物发现的重大前景.
- 挑战包括计算可扩展性,数据集成复杂性和生物解释性.
- 未来的工作应侧重于时间/空间动态,模型可解释性和标准化评估框架.
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