Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.
Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.
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DOI：
10.3389/fphar.2022.883433
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发表时间：
2022
影响因子：
5.6
通讯作者：
Edwards, Stephen
中科院分区：
文献类型：
--
作者：
Mandal, Meisha;Levy, Josh;Ives, Cataia;Hwang, Stephen;Zhou, Yi-Hui;Motsinger-Reif, Alison;Pan, Huaqin;Huggins, Wayne;Hamilton, Carol;Wright, Fred;Edwards, Stephen
及时且经济高效地测试化学品的需求推动了新替代方法 (NAM) 的开发，这些方法利用计算机和体外方法进行毒性预测。来自人类研究的大量现有数据可以帮助了解 NAM 支持化学品安全评估的能力。本研究旨在通过以编程方式识别每项研究中的相关变量来简化现有人类队列数据的整合。社区动脉粥样硬化风险 (ARIC) 研究中的研究变量根据其在研究中的相关性进行聚类。通过人工审核和自然语言处理（NLP）相结合的方式评估集群的质量。我们确定了 391 个聚类，包括 3,285 个变量。对包含多个变量的聚类的手动审核确定，人工审核者认为 95% 的聚类在某种程度上相关。为了评估人类审阅者的潜在偏差，还通过 NLP 对聚类进行评分，这显示出与人类分类的高度一致性。使用 Louvain 社区发现算法将聚类进一步合并为聚类组。对聚类组的手动审查证实，组内的聚类比来自不同组的聚类更具相关性。我们的数据驱动方法可以通过为人类注释者提供反映数据中存在的主题的相关变量组来促进数据协调和管理工作。审查相关变量组应该提高人工审查的效率，并且可以通过将策展人的注意力集中在主题与正在研究的主题相关的变量组上来减少审查的变量数量。
The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.
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影响因子：
3.9
作者：
Rashid SM;McCusker JP;Pinheiro P;Bax MP;Santos H;Stingone JA;Das AK;McGuinness DL
通讯作者：
McGuinness DL
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
DOI：
10.1080/10937404.2010.483176
发表时间：
2010-02
期刊：
Journal of toxicology and environmental health. Part B, Critical reviews
影响因子：
--
作者：
Krewski D;Acosta D Jr;Andersen M;Anderson H;Bailar JC 3rd;Boekelheide K;Brent R;Charnley G;Cheung VG;Green S Jr;Kelsey KT;Kerkvliet NI;Li AA;McCray L;Meyer O;Patterson RD;Pennie W;Scala RA;Solomon GM;Stephens M;Yager J;Zeise L
通讯作者：
Zeise L
影响因子：
4.6
作者：
Davis AP;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者：
Mattingly CJ
DOI：
10.1016/j.comtox.2020.100126
发表时间：
2020-08-01
期刊：
Computational toxicology (Amsterdam, Netherlands)
影响因子：
--
作者：
Ly Pham, Ly;Watford, Sean;Paul Friedman, Katie
通讯作者：
Paul Friedman, Katie