Personizing the prediction of future susceptibility to a specific disease.
Personizing the prediction of future susceptibility to a specific disease.
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DOI：
10.1371/journal.pone.0243127
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Spencer J
中科院分区：
文献类型：
--
作者：
Taha K;Davuluri R;Yoo P;Spencer J
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。检测到的可追踪生物标志物所属的这些分子途径的每种不同组合可以作为未来不同时间范围内疾病诱发的指示。基于这一概念，我们引入了一种新的方法来个性化一个人的未来对特定疾病的易感性程度。我们在一个名为疾病预测敏感度（SDDP）的工作系统中实施了该方法。对于特定的疾病d，设S为分子通路的集合，从d的大多数患者检测到的可追踪生物标志物属于该分子通路。对于同一种疾病d，设S′为分子通路的集合，从某个个体检测到的可追踪生物标志物属于这些分子通路。SDDP能够推断个体的未检测到的分子途径的子集S“”S {S-S“}。因此，SDDP可以基于从个体检测到的很少的分子途径来推断个体的疾病的未检测到的分子途径。SDDP还可以帮助推断集合{S′+S′′}中分子途径的组合，其可追踪的生物标志物共同指示疾病。SDDP由以下四个部分组成：信息提取器、分子途径建模器之间的相互关系、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来自动提取特定疾病的常见可追溯生物标志物。分子途径之间的相互关系建模器对可追踪的生物标志物的分子途径之间的分层相互关系进行建模。逻辑推理器将分子途径之间的层次相互关系转换为基于规则的规范。它采用谓词逻辑的规范规则和推理规则，为个体推断尽可能多的未检测到的疾病分子通路。风险指标输出反映个体未来对疾病的易感性程度的风险指标值。我们通过实验与其他方法进行比较来评估SDDP。结果显示有显著改善。
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.
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影响因子：
3.8
作者：
Frau, Francesca;Crowther, Daniel;Allebrandt, Karla V.
通讯作者：
Allebrandt, Karla V.
影响因子：
3
作者：
Dal Pal첫, A;Dovier, A;Fogolari, F
通讯作者：
Fogolari, F
DOI：
10.1093/bioinformatics/btp049
发表时间：
2009-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Hur J;Schuyler AD;States DJ;Feldman EL
通讯作者：
Feldman EL
影响因子：
3.7
作者：
Abbasi A;Sahlqvist AS;Lotta L;Brosnan JM;Vollenweider P;Giabbanelli P;Nunez DJ;Waterworth D;Scott RA;Langenberg C;Wareham NJ
通讯作者：
Wareham NJ
影响因子：
3.7
作者：
Jafari, Mohieddin;Ansari-Pour, Naser;Mirzaie, Mehdi
通讯作者：
Mirzaie, Mehdi