基于文献的提取和因果陈述的计算预测,将遗传变异与生物过程,途径和表型联系起来
Jici Jiang1, Predrag Radivojac2, Benjamin M Gyori3
1Northeastern University, Boston, MA 02115, United States.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
研究人员创建了一种新方法,利用自动化文本分析将遗传变异映射到疾病途径上. 这有助于理解突变如何导致疾病,并预测变异效应.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 了解遗传变异的功能对于疾病研究至关重要.
- 现有的资源缺乏全面的因果网络,将变异与生物结果联系起来.
- 需要自动化方法来从科学文献中组装这些复杂的网络.
研究的目的:
- 开发一种自动化的方法来重建遗传变异的因果网络.
- 建立一个文献广泛的资源,连接变体,分子途径和疾病表型.
- 为了实现机械解释和预测变异效应.
主要方法:
- 利用集成网络和动态推理组装器 (INDRA) 系统从文献中提取因果陈述.
- 重建了从遗传变异到生物过程,表型或疾病的定向路径.
- 训练了一种集成分子注释和蛋白质语言模型 (ESM2) 的分类模型,用于变异效应预测.
主要成果:
- 组装了一个有针对性的多图谱,其中有25862条路径用于2561种蛋白质的变异.
- 每个路径都链接了基于本体学的术语,并得到了文献证据的支持.
- 该分类模型在预测下游生物过程和疾病关联方面表现出有希望.
结论:
- 开发的框架使已知遗传变异的机制性解释成为可能.
- 它有助于预测具有有限前置注释的变体的功能相关性.
- 这一资源推动了对变异效应预测的理解,超出了简单的致病性评估.
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