一种知识图方法,通过生物医学报告阐明器官细胞通路在疾病中的作用
Alexander R Pelletier1, Dylan Steinecke2, Dibakar Sigdel3
1Department of Physiology, UCLA School of Medicine; Scalable Analytics Institute (ScAi) at Department of Computer Science, UCLA School of Engineering; NIH BRIDGE2AI Center at UCLA & NHLBI Integrated Cardiovascular Data Science Training Program, UCLA; arpelletier@g.ucla.edu.
Journal of visualized experiments : JoVE
|October 30, 2023
概括
CaseOLAP LIFT从生物医学文献中确定了蛋白质与疾病的关联. 这一计算管道增强了与疾病相关的细胞组件的发现,提高了研究可靠性.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 文本挖掘 (Text Mining) 是一种文字挖掘方式.
背景情况:
- 庞大的生物医学文献需要先进的文本挖掘工具.
- 发现蛋白质与疾病的关联对于理解病理学和治疗学至关重要.
研究的目的:
- 介绍CaseOLAP LIFT,这是一个用于提取和分析蛋白质疾病关联的计算管道.
- 从文本数据集中自动识别亚细胞蛋白,它们的功能伙伴和疾病相关性.
主要方法:
- 使用CaseOLAP LIFT来处理大型文本体,识别蛋白质与疾病的联系.
- 构建知识图表以将关联置于背景,并整合生物医学资源.
- 应用深度学习来预测新的蛋白质疾病关系.
主要成果:
- 通过使用3400万份文档库,成功阐明了线粒体蛋白在心血管疾病中的作用.
- 预测了1,583种新的蛋白质疾病关联,具有高可信度 (概率>0.90).
- 实现了 0.91 的接收器运行特征曲线 (AUROC) 下的区域,用于关系预测.
结论:
- CaseOLAP LIFT提供了一种可靠和自动化的方法来识别蛋白质与疾病的关联.
- 管道的可定制工作流程支持广泛的数据分析,并提高了研究可靠性.
- 促进对疾病病理学和潜在治疗方法的新见解的发现.
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