A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.
A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.
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DOI：
10.1016/j.envint.2022.107135
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发表时间：
2022-03
影响因子：
11.8
通讯作者：
Shao K
中科院分区：
文献类型：
--
作者：
Ji C;Weissmann A;Shao K
现有的研究表明，短期体内转录组学研究的基准剂量（BMD）估计值可以近似于长期指南毒性评估的结果。现有的软件应用程序遵循这一趋势，通过最大似然估计分析组学数据，并选择“最佳”模型进行BMD估计。然而，这种做法忽略了模型的不确定性，可能会导致过度自信的推断和预测，从而导致不充分的决策。通过遵循国家毒理学计划的基因组剂量-反应建模方法，我们开发了一个基于网络的剂量-反应建模和BMD估计系统，贝叶斯BMD（BBMD），用于基因组数据定量解决各种来源的不确定性。将BBMD的性能与BMDExpress进行了比较。该系统主要基于先前开发的BBMD系统，并在基因组的角度进一步发展。将贝叶斯模型平均法应用于骨密度估计和路径分析。一般来说，该系统在准备/存储数据和描述不确定性方面的灵活性是独一无二的。该系统与24个先前发表的体内微阵列剂量-反应数据集（GSE 45892）和来自Open TG-Gates数据库的64个分子数据进行了测试和验证。BBMD中位通路的短期转录BMD值与长期顶端BMD值高度相关（R = 0.78-0.91）。将BBMD获得的BMD估计值与BMDExpress获得的BMD估计值进行比较。结果表明，BBMD提供了更充分的结果，在较少的极端值，没有失败的BMD和BMDL计算。此外，BBMD中的途径分析提供了保守估计，因为建立了更宽的置信区间。总体而言，这项研究表明，使用基因组数据的剂量-反应模型可以在支持化学品风险评估方面发挥重要作用。BBMD代表了基因组剂量-反应数据分析的一种强大且用户友好的替代方案，具有出色的功能，可以量化各种来源的不确定性。
Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the “best” model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision. By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose–response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress. The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties. This system was tested and validated versus 24 previously published in-vivo microarray dose–response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78–0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established. Overall, this study demonstrates that dose–response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose–response data analysis with outstanding functionalities to quantify uncertainty from various sources.
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影响因子：
10.4
作者：
Shao K;Shapiro AJ
通讯作者：
Shapiro AJ
DOI：
10.1016/j.mrgentox.2012.01.007
发表时间：
2012-08-15
影响因子：
1.9
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
3.8
作者：
Thomas, Russell S.;Clewell, Harvey J., III;Andersen, Melvin E.
通讯作者：
Andersen, Melvin E.
影响因子：
3.8
作者：
Thomas, Russell S.;Wesselkamper, Scott C.;Andersen, Melvin E.
通讯作者：
Andersen, Melvin E.