Department of Electrical and Computer Science, Khalifa University, Abu Dhabi, UAE.
Department of Biomedical Informatics, School of Medicine and College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York, United States of America.
PLoS One. 2021 Jan 6;16(1):e0243127. doi: 10.1371/journal.pone.0243127. eCollection 2021.
A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.
可追踪的生物标志物是疾病分子途径的成员。一种疾病可能与几种分子途径有关。这些分子途径的每一种不同组合，以及所检测到的可追踪生物标志物所属的组合，都可能作为未来不同时间点疾病发生的指示。基于这一概念，我们提出了一种新的方法来个性化个体对特定疾病未来易感性的程度。我们在一个名为疾病易感性预测器（SDDP）的工作系统中实现了该方法。对于特定疾病 d，让 S 是从 d 的大多数患者中检测到的可追踪生物标志物所属的分子途径集。对于同一疾病 d，让 S' 是从特定个体中检测到的可追踪生物标志物所属的分子途径集。SDDP 能够推断出个体的未检测分子途径子集 S'' ⊆{S-S'}。因此，SDDP 可以根据从个体中检测到的少数分子途径推断出个体疾病的未检测分子途径。SDDP 还可以帮助推断集合{S'+S''}中的分子途径组合，其可追踪生物标志物共同指示疾病。SDDP 由以下四个组件组成：信息提取器、分子途径模型关系模型、逻辑推理器和风险指示器。信息提取器利用生物医学文献的指数增长，自动提取特定疾病的常见可追踪生物标志物。分子途径模型关系模型对可追踪生物标志物的分子途径之间的层次关系进行建模。逻辑推理器将分子途径之间的层次关系转换为基于规则的规范。它采用规范规则和谓词逻辑的推理规则，尽可能多地为个体推断出疾病的未检测分子途径。风险指示器输出一个风险指示器值，反映个体对疾病未来易感性的程度。我们通过与其他方法进行实验比较来评估 SDDP。结果显示出显著的改善。
