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置信度预测皮肤致敏物——使用共形预测确定 GARD 的适用域。

Predicting skin sensitizers with confidence - Using conformal prediction to determine applicability domain of GARD.

机构信息

Department of Immunotechnology, Lund University, Medicon Village (406), 22381 Lund, Sweden.

Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Forskargatan 20, SE-151 36 Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Box 7003, SE-164 07 Kista, Sweden.

出版信息

Toxicol In Vitro. 2018 Apr;48:179-187. doi: 10.1016/j.tiv.2018.01.021. Epub 2018 Jan 31.

Abstract

GARD - Genomic Allergen Rapid Detection is a cell based alternative to animal testing for identification of skin sensitizers. The assay is based on a biomarker signature comprising 200 genes measured in an in vitro model of dendritic cells following chemical stimulations, and consistently reports predictive performances ~90% for classification of external test sets. Within the field of in vitro skin sensitization testing, definition of applicability domain is often neglected by test developers, and assays are often considered applicable across the entire chemical space. This study complements previous assessments of model performance with an estimate of confidence in individual classifications, as well as a statistically valid determination of the applicability domain for the GARD assay. Conformal prediction was implemented into current GARD protocols, and a large external test dataset (n = 70) was classified at a confidence level of 85%, to generate a valid model with a balanced accuracy of 88%, with none of the tested chemical reactivity domains identified as outside the applicability domain of the assay. In conclusion, results presented in this study complement previously reported predictive performances of GARD with a statistically valid assessment of uncertainty in each individual prediction, thus allowing for classification of skin sensitizers with confidence.

摘要

GARD - 基因过敏原快速检测是一种基于细胞的替代动物测试方法,用于识别皮肤致敏剂。该测定法基于生物标志物特征,该特征由 200 个基因组成,在经过化学刺激的树突状细胞体外模型中进行测量,并且对于外部测试集的分类,预测性能始终保持在约 90%左右。在体外皮肤致敏测试领域,测试开发人员通常忽略适用性域的定义,并且通常认为测试适用于整个化学空间。本研究通过对个体分类的置信度估计以及对 GARD 测定法适用性域的统计学有效确定,对模型性能的先前评估进行了补充。共形预测已被纳入当前的 GARD 方案中,并且对一个大型外部测试数据集(n=70)进行了分类,置信度为 85%,从而生成了一个具有 88%平衡准确性的有效模型,没有一个测试的化学反应性域被确定为超出了测定法的适用性域。总之,本研究的结果通过对每个个体预测的不确定性进行统计学有效评估,对 GARD 的先前报告的预测性能进行了补充,从而可以有信心地对皮肤致敏剂进行分类。

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