Computer, Electrical and Mathematical Sciences & Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge CB2 3EG, United Kingdom.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae639.
Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks. Such causal relations can facilitate the identification of potential disease precursors and candidates for drug re-purposing. However, computational methods often lack access to these causal relations. Few approaches have been developed to automatically extract causal relationships between diseases from unstructured text, but they are often only focused on a small number of diseases, lack validation of the extracted causal relations, or do not make their data available.
We automatically mined statements asserting a causal relation between diseases from the scientific literature by leveraging lexical patterns. Following automated mining of causal relations, we mapped the diseases to the International Classification of Diseases (ICD) identifiers to allow the direct application to clinical data. We provide quantitative and qualitative measures to evaluate the mined causal relations and compare to UK Biobank diagnosis data as a completely independent data source. The validated causal associations were used to create a directed acyclic graph that can be used by causal inference frameworks. We demonstrate the utility of our causal network by performing causal inference using the do-calculus, using relations within the graph to construct and improve polygenic risk scores, and disentangle the pleiotropic effects of variants.
The data are available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases.
识别疾病之间的因果关系可以研究共同的途径、生物学机制和疾病间的风险。这些因果关系可以帮助识别潜在的疾病前兆和药物再利用的候选者。然而，计算方法通常无法获得这些因果关系。已经开发了一些从非结构化文本中自动提取疾病之间因果关系的方法，但它们通常只关注少数几种疾病，缺乏对提取的因果关系的验证，或者没有公开其数据。
我们通过利用词汇模式，从科学文献中自动挖掘断言疾病之间因果关系的陈述。在自动挖掘因果关系之后，我们将疾病映射到国际疾病分类（ICD）标识符，以允许直接应用于临床数据。我们提供了定量和定性的度量标准来评估挖掘出的因果关系，并与英国生物库（UK Biobank）的诊断数据进行比较，UK Biobank 是一个完全独立的数据源。经过验证的因果关联被用来创建一个有向无环图，该图可以被因果推理框架使用。我们通过使用 do 演算在因果网络上进行因果推理，使用图中的关系来构建和改进多基因风险评分，并分解变体的多效性效应，展示了我们因果网络的实用性。
数据可通过 https://github.com/bio-ontology-research-group/causal-relations-between-diseases 获得。
