Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.
Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.
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DOI：
10.1016/j.jhazmat.2021.127141
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发表时间：
2022-02-05
影响因子：
13.6
通讯作者：
Gu, April Z.
中科院分区：
文献类型：
--
作者：
Rahman, Sheikh Mokhlesur;Lan, Jiaqi;Kaeli, David;Dy, Jennifer;Alshawabkeh, Akram;Gu, April Z.
实现和实施Tox21愿景的主要挑战之一是迫切需要在体外检测分子终点和体内调节相关表型毒性终点之间建立定量联系。目前的毒性组学方法仍然主要依赖于大量冗余标记，没有预先选择或排序，因此，选择冗余最少的相关生物标记将减少需要监测的标记数量，降低毒性筛选和风险监测的成本、时间和复杂性。在这里，我们证明，使用时间序列体外毒性组学分析以及基于机器学习的特征选择（最大相关性和最小冗余（MRMR））和分类方法（支持向量机（SVM）），可以确定具有最小冗余的生物标志物的“最佳”数量，以良好的准确性预测表型毒性终点。我们纳入了两个体内致癌性和Ames遗传毒性预测的案例研究，分别使用了20种选定的化学物质，包括模型遗传毒性化学物质和阴性对照。结果表明，采用不良结局途径（AOP）概念，基于相对少量的正确选择的生物标志物集合参与真核生物中保守的dna损伤和修复途径的分子终点，能够预测大鼠的Ames遗传毒性终点和体内致癌性。在使用排名前五的生物标志物预测体内致癌性时，预测准确率达到76%，AUC = 0.81。对于Ames遗传毒性预测，排名前5位的生物标志物能够达到70%的预测精度，AUC = 0.75。然而，在两种不同的表型遗传毒性试验中，被确定为排名前五的生物标志物的特异性生物标志物是不同的。在体内致癌性预测中，排名靠前的生物标志物主要集中在双链断裂修复和DNA重组上，而在Ames遗传毒性预测中，排名靠前的生物标志物主要与碱基和核苷酸切除修复有关。本研究开发的方法将有助于填补表型锚定和预测毒理学方面的知识空白。并促进在实施tox 21环境和健康应用愿景方面取得进展。
One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an “optimal” number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction, using 20 selected chemicals including model genotoxic chemicals and negative controls, respectively. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both Ames genotoxicity endpoints and in-vivo carcinogenicity in rats. A prediction accuracy of 76% with AUC = 0.81 was achieved while predicting in-vivo carcinogenicity with the top-ranked five biomarkers. For Ames genotoxicity prediction, the top-ranked five biomarkers were able to achieve prediction accuracy of 70% with AUC = 0.75. However, the specific biomarkers identified as the top-ranked five biomarkers are different for the two different phenotypic genotoxicity assays. The top-ranked biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications.
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DOI：
10.1093/toxsci/kfx097
发表时间：
2017-08-01
期刊：
Toxicological sciences : an official journal of the Society of Toxicology
影响因子：
--
作者：
Brockmeier EK;Hodges G;Hutchinson TH;Butler E;Hecker M;Tollefsen KE;Garcia-Reyero N;Kille P;Becker D;Chipman K;Colbourne J;Collette TW;Cossins A;Cronin M;Graystock P;Gutsell S;Knapen D;Katsiadaki I;Lange A;Marshall S;Owen SF;Perkins EJ;Plaistow S;Schroeder A;Taylor D;Viant M;Ankley G;Falciani F
通讯作者：
Falciani F
DOI：
10.1016/j.scitotenv.2018.02.015
发表时间：
2018-07-01
期刊：
The Science of the total environment
影响因子：
--
作者：
Carusi A;Davies MR;De Grandis G;Escher BI;Hodges G;Leung KMY;Whelan M;Willett C;Ankley GT
通讯作者：
Ankley GT
影响因子：
11.4
作者：
Bradbury, SP;Feijtel, TCJ;Van Leeuwen, CJ
通讯作者：
Van Leeuwen, CJ
影响因子：
11.4
作者：
Conolly, Rory B.;Ankley, Gerald T.;Watanabe, Karen H.
通讯作者：
Watanabe, Karen H.
DOI：
10.1073/pnas.70.8.2281
发表时间：
1973-01-01
影响因子：
11.1
作者：
AMES, BN;DURSTON, WE;LEE, FD
通讯作者：
LEE, FD