从文献中挖掘出来的疾病之间的因果关系改善了使用多基因风险评分的使用
Sumyyah Toonsi1, Iris Ivy Gauran1, Hernando Ombao1
1Computer, Electrical and Mathematical Sciences & Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
Bioinformatics (Oxford, England)
|October 26, 2024
概括
我们开发了一种方法,可以自动从科学文献中提取疾病之间的因果关系. 这使得人们能够更好地了解疾病途径和风险,并可获得用于临床应用的验证数据.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 确定病因关系有助于理解共同的途径和疾病间的风险.
- 因果关系可以识别疾病前体和药物重新定位候选人.
- 现有的计算方法难以从文本中访问和验证病因关系.
研究的目的:
- 从科学文献中自动提取和验证疾病之间的因果关系.
- 为临床应用创建一个全面的,经过验证的疾病因果关系网络.
- 将提取的因果关系数据公开提供.
主要方法:
- 利用词汇模式来挖掘断言,断言疾病之间的因果关系.
- 将已识别的疾病映射到国际疾病分类 (ICD) 标识符.
- 使用定量和定性措施验证挖掘的因果关系,包括英国生物银行数据.
主要成果:
- 从科学文献中成功地挖掘出疾病之间的因果关系.
- 创建了一个经过验证的疾病因果关系的定向非循环图.
- 在因果推断,改善多基因风险得分和解开类效应方面证明有用.
结论:
- 自动提取和验证疾病因果关系是可行的和有价值的.
- 开发出来的因果网络增强了因果推断和遗传分析.
- 公开可用的数据有助于进一步研究疾病机制和临床应用.
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