综合边缘信息和药物疾病关联路径拓
Xianbin Li1,2, Xiangzhen Zan3, Tao Liu1
1School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China.
iScience
|July 8, 2024
概括
这项研究引入了iEdgePathDDA,这是一种用于药物重定向的新型计算方法,它分析了途径内的基因相互作用变化. 它在识别各种癌症的潜在抗癌药物方面优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学 是一个学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物的重新用途加速了对现有药物的新治疗用途的发现.
- 目前用于抗癌药物发现的计算方法往往忽略了基因相互作用动态.
- 识别新的药物疾病关联对于有效的药物开发至关重要.
研究的目的:
- 开发一种计算药物重定位方法,iEdgePathDDA,它包含边缘级基因相互作用信息和通路拓.
- 通过分析药物诱导和与疾病相关的基因相互作用变化,优先考虑候选抗癌药物.
- 评估iEdgePathDDA的性能与最先进的方法相比.
主要方法:
- 开发了iEdgePathDDA,一种专注于边缘信息和路径拓学的计算方法.
- 使用皮尔森相关系数识别了药物诱导和与疾病相关的边缘.
- 计算药物诱导和疾病相关边缘之间的抑制得分,以优先考虑候选药物.
主要成果:
- iEdgePathDDA成功地确定了新的药物-疾病对,并使用比较毒基因组学数据库 (CTD) 进行了验证.
- 与现有方法相比,该方法在结肠直肠,乳腺和肺癌数据集中表现出卓越的性能.
- 使用五个关键指标评估绩效,突出方法的有效性.
结论:
- 通过整合边缘级基因相互作用数据,iEdgePathDDA在计算药物重定位方面取得了重大进展.
- 该方法为识别癌症治疗的有前途的候选药物提供了一个强大的框架.
- 这种方法提高了对已批准药物的新疗法发现的效率和准确性.
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