计算协议用于识别疾病之间共享的转录风险和互惠的化合物
Hua Gao1, Mao Zhang2, Richard A Baylis3
1Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA.
STAR protocols
|February 14, 2024
概括
这项研究引入了一个计算协议,以找到共享的疾病机制,并选药物进行重新使用. 它使用omics数据和电子健康记录来识别新疗法的有益化合物.
科学领域:
- 基因组学和生物信息学
- 翻译医学是一种翻译医学.
- 计算生物学 计算生物学
背景情况:
- 越来越多的omics和生物银行数据使得共享疾病途径的全基因组分析成为可能.
- 药物重新定位通过利用现有化合物,为新疗法提供更快的途径.
研究的目的:
- 提出一种用于识别疾病之间共享的转录过程的计算协议.
- 选化合物以寻找潜在的治疗益处和药物重新用途的机会.
- 用现实世界电子健康记录来描述用于验证化合物效应的药监测研究.
主要方法:
- 实现用于计算分析的Snakemake工作流程.
- 利用omics数据和生物库资源来获得全基因组的洞察力.
- 使用电子健康记录用于药物监督和验证.
主要成果:
- 在疾病中识别共享的转录过程.
- 对化合物进行选,以寻找潜在的相互治疗益处.
- 药物效应的验证框架使用真实世界的数据.
结论:
- 该协议有助于发现共享的疾病机制和新的治疗策略.
- 它可以有效地选化合物用于药物重新用途.
- 与药监测研究的整合增强了已识别的化合物的验证.
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