论文标题
心脏血管疾病的个性化病理测试：近似贝叶斯计算与歧视性统计数据学习
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
论文作者
论文摘要
心脏/脑血管疾病（CVD）已成为我们社会中的主要健康问题之一。但是最近的研究表明，目前检测CVD的病理测试是无效的，因为它们不考虑血小板激活的不同阶段或与血小板相互作用有关的分子动力学，并且无法考虑个体间的变异性。在这里，我们提出了一个随机血小板沉积模型和推论方案，使用近似贝叶斯计算，并具有摘要统计量，以最大程度地区分不同类型的患者。从健康志愿者和不同患者类型的数据收集的数据中推断出的参数有助于我们确定特定的生物学参数，从而为每种类型的患者提供了功能障碍背后的生物学推理。这项工作为CVD检测和医疗的个性化病理测试打开了前所未有的机会。
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
