心血管疾病的多式视觉知识图
Khaled Rjoob1, Kathryn A McGurk1,2, Sean L Zheng1,2
1MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
CardioKG是一种新的知识图,将心血管成像数据与生物数据库集成在一起,以预测基因疾病的联系,并确定心脏病的新药疗法. 这种方法增强了对可治疗心血管疾病机制的理解.
科学领域:
- 生物医学信息学 生物医学信息学
- 心血管研究研究心血管研究
- 计算生物学 计算生物学
背景情况:
- 基因与疾病的关联对于理解疾病机制和找到治疗点至关重要.
- 现有的知识图缺乏关于器官结构和功能的个体级数据.
- 生物医学成像提供了丰富的表型信息,并未完全集成到当前的模型中.
研究的目的:
- 开发 CardioKG,一个知识图集心血管成像表型与生物数据.
- 利用图形嵌入来预测基因疾病关联和治疗机会.
- 利用综合数据,探索用于心血管疾病的药物重定向策略.
主要方法:
- 从图像中整合了超过20万个计算机视觉衍生的心血管表型.
- 将成像数据与来自18个生物数据库的信息结合起来.
- 在节点嵌入和预测任务中使用变量图形自动编码器.
主要成果:
- 在 CardioKG 知识图中建模了超过一百万种关系.
- 成功预测了基因与疾病的关联,并确定了潜在的候选药物.
- 确定了用于心力衰竭的甲状腺素和用于心房的平素作为候选疗法.
- 证明成像数据集成可以增强途径发现和治疗机会的识别.
结论:
- 卡迪奥KG有效地整合了各种生物医学数据,包括成像表型.
- 开发的基于图形的模型有助于发现可治疗的心血管疾病机制.
- 生物医学成像数据显著增强了用于治疗目标识别的图形结构模型.
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