Pub Date : 2021-08-24DOI: 10.1201/9781003158745-12
C. Giraud
Background: Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. Results: From a comprehensive search of the MeSH annotation of MEDLINE articles, we identified 3,342 environmental etiological factors associated with 3,159 diseases. We also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the "etiome", which we define as the comprehensive compendium of disease etiology. Clustering of environmental factors may alert clinicians of the risks of added exposures, or synergy in interventions to alter these factors. Clustering of both genetic and environmental etiological factors puts genes in the context of environment in a quantitative manner. Conclusion: In this paper, we obtained a comprehensive list of associations between disease and environmental factors using MeSH annotation of MEDLINE articles. It serves as a summary of current knowledge between etiological factors and diseases. By combining the environmental etiological factors and genetic factors from GAD, we computed the "etiome" profile for 863 diseases. Comparing diseases across these profiles may have utility for clinical medicine, basic science research, and population-based science.
{"title":"Clustering","authors":"C. Giraud","doi":"10.1201/9781003158745-12","DOIUrl":"https://doi.org/10.1201/9781003158745-12","url":null,"abstract":"Background: Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. Results: From a comprehensive search of the MeSH annotation of MEDLINE articles, we identified 3,342 environmental etiological factors associated with 3,159 diseases. We also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the \"etiome\", which we define as the comprehensive compendium of disease etiology. Clustering of environmental factors may alert clinicians of the risks of added exposures, or synergy in interventions to alter these factors. Clustering of both genetic and environmental etiological factors puts genes in the context of environment in a quantitative manner. Conclusion: In this paper, we obtained a comprehensive list of associations between disease and environmental factors using MeSH annotation of MEDLINE articles. It serves as a summary of current knowledge between etiological factors and diseases. By combining the environmental etiological factors and genetic factors from GAD, we computed the \"etiome\" profile for 863 diseases. Comparing diseases across these profiles may have utility for clinical medicine, basic science research, and population-based science.","PeriodicalId":119847,"journal":{"name":"Introduction to High-Dimensional Statistics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122409375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. The paper presents recent developments of the theory of estimator selection. We introduce, in the density estimation framework, the main methods used by the participants of the session "Variable and estimator selection" in the Journées MAS 2014. The purpose of the selection is always to prove oracle inequalities, that is, to compare the selected estimator with the best estimator in the original collection via some risk function. The first part of the paper deals with the selection by minimization of a penalized empirical loss and the second presents the methods based on robust tests. Résumé. L’article présente quelques développements récents de la théorie de la sélection d’estimateurs. Nous introduisons, dans le cadre élémentaire de l’estimation de la densité, les principales méthodes ap-parues dans les exposés de la session "Sélections de variables, sélection d’estimateurs" des Journées MAS 2014. L’objectif de la sélection est toujours l’obtention d’inégalités oracle comparant le risque de l’estimateur choisi au plus petit des risques des estimateurs de la collection initiale. Nous discuterons dans une première partie les méthodes par minimisation d’un critère pénalisé et dans une seconde celles utilisant les tests robustes.
。The paper presents recent developments of The theory of estimator选择。在密度估计框架中，我们介绍了2014年MAS日“变量和估计量选择”会议参与者使用的主要方法。oracle的目的》选择is always to prove inequalities, that is to The estimator选编》比作with The best estimator透过诗risk in The original收藏功能。秋季with The rstfipart of The paper》by minimization of a penalized经验性选择loss and The second presents The方法基于鲁棒测试。摘要。本文介绍了估计量选择理论的一些最新发展。在密度估计的基本框架内，我们介绍了在MAS 2014“变量选择，估计量选择”会议上发表的主要方法。选择的目的总是通过比较所选估计器的风险与初始集合估计器的最小风险来获得oracle不等式。在第一部分中，我们将讨论最小化惩罚标准的方法，在第二部分中，我们将讨论使用鲁棒测试的方法。
{"title":"Estimator Selection","authors":"C. Giraud","doi":"10.1201/9781003158745-7","DOIUrl":"https://doi.org/10.1201/9781003158745-7","url":null,"abstract":". The paper presents recent developments of the theory of estimator selection. We introduce, in the density estimation framework, the main methods used by the participants of the session \"Variable and estimator selection\" in the Journées MAS 2014. The purpose of the selection is always to prove oracle inequalities, that is, to compare the selected estimator with the best estimator in the original collection via some risk function. The first part of the paper deals with the selection by minimization of a penalized empirical loss and the second presents the methods based on robust tests. Résumé. L’article présente quelques développements récents de la théorie de la sélection d’estimateurs. Nous introduisons, dans le cadre élémentaire de l’estimation de la densité, les principales méthodes ap-parues dans les exposés de la session \"Sélections de variables, sélection d’estimateurs\" des Journées MAS 2014. L’objectif de la sélection est toujours l’obtention d’inégalités oracle comparant le risque de l’estimateur choisi au plus petit des risques des estimateurs de la collection initiale. Nous discuterons dans une première partie les méthodes par minimisation d’un critère pénalisé et dans une seconde celles utilisant les tests robustes.","PeriodicalId":119847,"journal":{"name":"Introduction to High-Dimensional Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125506627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Aggregation of Estimators","authors":"C. Giraud","doi":"10.1201/B17895-6","DOIUrl":"https://doi.org/10.1201/B17895-6","url":null,"abstract":"","PeriodicalId":119847,"journal":{"name":"Introduction to High-Dimensional Statistics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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