Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them...Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.展开更多
背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://...背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://gwas.mrcieu.ac.uk/,由英国布里斯托大学医学研究委员会综合流行病学单位主办)中获得炎症细胞因子及脑卒中的数据,使用逆方差加权法作为主要研究方法进行两样本孟德尔随机化分析,评估91种炎症细胞因子与脑卒中之间的因果关系。随后基于孟德尔随机化研究结果进行了基因本体分析和京都基因与基因组通路分析,并构建了蛋白质-蛋白质相互作用网络。使用美国西奈山伊坎医学院建立的Enrichr数据库(http://amp.pharm.mssm.edu/Enrichr)和美国科罗拉多大学丹佛分校建立的药物特征数据库(http://tanlab.ucdenver.edu/dsigdb)进行脑卒中治疗药物预测,并使用AutoDock软件进行分子对接,通过Discovery Studio2019对结果进行可视化。结果与结论:(1)发现11种炎症细胞因子与全因脑卒中风险之间存在显著的因果关联;9种炎症细胞因子与缺血性脑卒中风险呈强相关;6种细胞因子与大动脉脑卒中风险显著相关;7种炎症细胞因子与心源性栓塞性脑卒中风险呈显著因果关系;12种细胞因子与小血管脑卒中风险显著相关;3种炎症细胞因子与脑内出血风险具有显著的因果关联;(2)基因本体分析和京都基因与基因组通路分析揭示,炎症细胞因子在代谢、炎症及免疫反应等方面对脑卒中具有重要影响;(3)通过蛋白质-蛋白质相互作用网络分析,筛选出与脑卒中密切相关的10种炎症细胞因子;(4)药物预测和分子对接结果表明,阿托伐他汀和氟氢可的松与关键核心靶点白细胞介素18和CCL3的结合力较高;(5)此次研究的数据来源于国际数据库中的欧洲人群,所获得的结果可为中国脑卒中的遗传流行病学研究提供参考;(6)此次研究阐明了炎症细胞因子与脑卒中之间的因果关系,揭示了炎症细胞因子治疗脑卒中的机制,为脑卒中的治疗提供了新思路。展开更多
Sterculia gum,the dry exudate of Sterculia versicolor and other members of the same genus,is used as a thickener and emulsifier in foods.It is generally considered safe as a food or drug,and its adverse reactions,such...Sterculia gum,the dry exudate of Sterculia versicolor and other members of the same genus,is used as a thickener and emulsifier in foods.It is generally considered safe as a food or drug,and its adverse reactions,such as Sterculia-induced liver injury,have never been reported.A 46-year-old woman was admitted to hospital with fatigue,nausea,abdominal distension,jaundice and a>16-fold increase in transaminase and bilirubin level.The patient had used Sterculia gum prior to the onset of her symptoms.Her symptoms and clinical indicators improved after treatment.The possibility of acute viral hepatitis,autoimmune hepatitis,and metabolic liver disease was excluded.After discharge from hospital,the patient had a severe liver injury again when re-exposed to Sterculia gum.And the Roussel Uclaf Causality Assessment Method score was updated from 5 to 7,which was consistent with probable drug-induced liver injury.This is the first report of Sterculia-induced liver injury.Clinicians need to be aware of the potential hepatotoxicity of Sterculia.展开更多
We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is differen...We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.展开更多
背景:研究表明肠道菌群可能会影响类风湿关节炎的发展进程,然而,两者之间的因果关系尚不清楚。使用公开发表的GWAS数据对两者进行孟德尔随机化分析可探讨肠道菌群与类风湿关节炎之间的因果关系,有助于开发针对性的微生物疗法,为类风湿...背景:研究表明肠道菌群可能会影响类风湿关节炎的发展进程,然而,两者之间的因果关系尚不清楚。使用公开发表的GWAS数据对两者进行孟德尔随机化分析可探讨肠道菌群与类风湿关节炎之间的因果关系,有助于开发针对性的微生物疗法,为类风湿关节炎的预防和治疗提供方法和策略。目的:采用两样本双向孟德尔随机化方法探讨肠道菌群与类风湿关节炎之间的潜在因果关系。方法:使用MiBio-Gen联盟的肠道菌群全基因组关联研究(GWAS)数据和IEU Open GWAS数据库(英国布里斯托尔大学和流行病学部门共同开发的大型基因-表型关联数据库)的类风湿关节炎GWAS数据,以逆方差加权法为主要分析方法,MR-Egger回归法、加权中位数法、加权模型法和简单模型法为补充来研究肠道菌群与类风湿关节炎之间的因果关系。使用Cochran’s Q检验评估异质性,MR-PRESSO和MR-Egger intercept检验评估水平多效性,留一法检验结果的稳健性,反向孟德尔随机分析评估肠道菌群与类风湿关节炎是否存在反向因果关系。结果与结论:①正向孟德尔随机化逆方差加权法分析结果显示,5种肠道菌群与类风湿关节炎存在因果关系,其中瘤胃球菌属(β=0.262,OR=1.300,P=0.013)、丁酸梭菌属(β=0.001,OR=1.001,P=0.014)会增加类风湿关节炎的发病风险,厌氧斯氏菌属(β=-0.225,OR=0.798,P=0.025)、毛螺菌属-UCG010(β=-0.177,OR=0.838,P=0.026)和草酸杆菌属(β=-0.171,OR=0.843,P=0.001)可以降低类风湿关节炎的发病风险;敏感性分析未见显著异质性和水平多效性(P均>0.05),留一法检测证实了结果的稳健性,而逆方差加权法之外的其余4种方法的补充进一步验证了结果的可靠性与稳定性。②反向孟德尔随机化分析未发现类风湿关节炎与正向孟德尔随机化确定的5类肠道菌有因果关系。③结果表明,瘤胃球菌属、丁酸梭菌属可能是类风湿关节炎的危险因素,厌氧斯氏菌属、毛螺菌属-UCG010和草酸杆菌属可能是类风湿关节炎的保护因素。肠道菌群在类风湿关节炎的发病机制中可能发挥重要作用,为类风湿关节炎的预防与治疗提供了新的生物标志物。针对中国生物医学研究领域,可以借鉴国际经验,逐步建立和完善多中心的大规模遗传数据库,从而深入探讨肠道菌群与疾病风险之间的关系,推动中国精准医疗和个性化治疗的发展。展开更多
We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavi...We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.展开更多
文摘Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.
文摘背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://gwas.mrcieu.ac.uk/,由英国布里斯托大学医学研究委员会综合流行病学单位主办)中获得炎症细胞因子及脑卒中的数据,使用逆方差加权法作为主要研究方法进行两样本孟德尔随机化分析,评估91种炎症细胞因子与脑卒中之间的因果关系。随后基于孟德尔随机化研究结果进行了基因本体分析和京都基因与基因组通路分析,并构建了蛋白质-蛋白质相互作用网络。使用美国西奈山伊坎医学院建立的Enrichr数据库(http://amp.pharm.mssm.edu/Enrichr)和美国科罗拉多大学丹佛分校建立的药物特征数据库(http://tanlab.ucdenver.edu/dsigdb)进行脑卒中治疗药物预测,并使用AutoDock软件进行分子对接,通过Discovery Studio2019对结果进行可视化。结果与结论:(1)发现11种炎症细胞因子与全因脑卒中风险之间存在显著的因果关联;9种炎症细胞因子与缺血性脑卒中风险呈强相关;6种细胞因子与大动脉脑卒中风险显著相关;7种炎症细胞因子与心源性栓塞性脑卒中风险呈显著因果关系;12种细胞因子与小血管脑卒中风险显著相关;3种炎症细胞因子与脑内出血风险具有显著的因果关联;(2)基因本体分析和京都基因与基因组通路分析揭示,炎症细胞因子在代谢、炎症及免疫反应等方面对脑卒中具有重要影响;(3)通过蛋白质-蛋白质相互作用网络分析,筛选出与脑卒中密切相关的10种炎症细胞因子;(4)药物预测和分子对接结果表明,阿托伐他汀和氟氢可的松与关键核心靶点白细胞介素18和CCL3的结合力较高;(5)此次研究的数据来源于国际数据库中的欧洲人群,所获得的结果可为中国脑卒中的遗传流行病学研究提供参考;(6)此次研究阐明了炎症细胞因子与脑卒中之间的因果关系,揭示了炎症细胞因子治疗脑卒中的机制,为脑卒中的治疗提供了新思路。
基金the support of Yunnan (Kunming) Zhang Wenhong expert workstation (YSZJGZZ-2020051)
文摘Sterculia gum,the dry exudate of Sterculia versicolor and other members of the same genus,is used as a thickener and emulsifier in foods.It is generally considered safe as a food or drug,and its adverse reactions,such as Sterculia-induced liver injury,have never been reported.A 46-year-old woman was admitted to hospital with fatigue,nausea,abdominal distension,jaundice and a>16-fold increase in transaminase and bilirubin level.The patient had used Sterculia gum prior to the onset of her symptoms.Her symptoms and clinical indicators improved after treatment.The possibility of acute viral hepatitis,autoimmune hepatitis,and metabolic liver disease was excluded.After discharge from hospital,the patient had a severe liver injury again when re-exposed to Sterculia gum.And the Roussel Uclaf Causality Assessment Method score was updated from 5 to 7,which was consistent with probable drug-induced liver injury.This is the first report of Sterculia-induced liver injury.Clinicians need to be aware of the potential hepatotoxicity of Sterculia.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60904039)
文摘We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.
文摘背景:研究表明肠道菌群可能会影响类风湿关节炎的发展进程,然而,两者之间的因果关系尚不清楚。使用公开发表的GWAS数据对两者进行孟德尔随机化分析可探讨肠道菌群与类风湿关节炎之间的因果关系,有助于开发针对性的微生物疗法,为类风湿关节炎的预防和治疗提供方法和策略。目的:采用两样本双向孟德尔随机化方法探讨肠道菌群与类风湿关节炎之间的潜在因果关系。方法:使用MiBio-Gen联盟的肠道菌群全基因组关联研究(GWAS)数据和IEU Open GWAS数据库(英国布里斯托尔大学和流行病学部门共同开发的大型基因-表型关联数据库)的类风湿关节炎GWAS数据,以逆方差加权法为主要分析方法,MR-Egger回归法、加权中位数法、加权模型法和简单模型法为补充来研究肠道菌群与类风湿关节炎之间的因果关系。使用Cochran’s Q检验评估异质性,MR-PRESSO和MR-Egger intercept检验评估水平多效性,留一法检验结果的稳健性,反向孟德尔随机分析评估肠道菌群与类风湿关节炎是否存在反向因果关系。结果与结论:①正向孟德尔随机化逆方差加权法分析结果显示,5种肠道菌群与类风湿关节炎存在因果关系,其中瘤胃球菌属(β=0.262,OR=1.300,P=0.013)、丁酸梭菌属(β=0.001,OR=1.001,P=0.014)会增加类风湿关节炎的发病风险,厌氧斯氏菌属(β=-0.225,OR=0.798,P=0.025)、毛螺菌属-UCG010(β=-0.177,OR=0.838,P=0.026)和草酸杆菌属(β=-0.171,OR=0.843,P=0.001)可以降低类风湿关节炎的发病风险;敏感性分析未见显著异质性和水平多效性(P均>0.05),留一法检测证实了结果的稳健性,而逆方差加权法之外的其余4种方法的补充进一步验证了结果的可靠性与稳定性。②反向孟德尔随机化分析未发现类风湿关节炎与正向孟德尔随机化确定的5类肠道菌有因果关系。③结果表明,瘤胃球菌属、丁酸梭菌属可能是类风湿关节炎的危险因素,厌氧斯氏菌属、毛螺菌属-UCG010和草酸杆菌属可能是类风湿关节炎的保护因素。肠道菌群在类风湿关节炎的发病机制中可能发挥重要作用,为类风湿关节炎的预防与治疗提供了新的生物标志物。针对中国生物医学研究领域,可以借鉴国际经验,逐步建立和完善多中心的大规模遗传数据库,从而深入探讨肠道菌群与疾病风险之间的关系,推动中国精准医疗和个性化治疗的发展。
文摘We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
