Signposts for Aquatic Toxicity Evaluation in China: Text Mining using Event-Driven Taxonomy within and among Regions
Signposts for Aquatic Toxicity Evaluation in China: Text Mining using Event-Driven Taxonomy within and among Regions
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DOI：
10.1021/acs.est.1c00152
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发表时间：
2021-06-18
影响因子：
11.4
通讯作者：
You, Jing
中科院分区：
文献类型：
--
作者：
Cheng, Fei;Li, Huizhen;You, Jing
毒性终点的选择影响风险评估的结果。基于更全面的证据的科学决策更适合设计生物测定电池，而不是主观选择，特别是当系统知之甚少时。在这里，我们提出了一种新的事件驱动分类（EDT）为基础的文本挖掘工具，优先考虑可能引起水质恶化的压力。该工具集成了自动化文献收集、使用基于不良结局途径的毒理学术语的自然语言处理和机器学习来对事件驱动因素（ED）进行分类。从过去十年中国的水生毒性评估中，我们收集了14000多个信息来源。通过一本包括1039个毒理学术语的词典，绘制了15种生物测定相关的作用模式，但只有不到一半的生物测定可以通过可用的不良结果途径来阐明。为了填补这些机械知识的空白，我们开发了一个朴素贝叶斯ED分类器来注释心尖反应。分类器的4倍交叉验证达到74%的准确度，并将85%的生物测定标记为26个ED。麻醉，雌激素受体，芳基氢受体介导剂是中国各地的水生系统中的主要ED，而个别地区有不同的艾德指纹。基于EDT的工具提供了一种有前途的诊断策略，为大数据时代的水质评估提供区域特定的生物测定设计和选择。
Selection of toxicity endpoints affects outcomes of risk assessment. Scientific decisions based on more holistic evidence is preferable for designing bioassay batteries rather than subjective selections, particularly when systems are poorly understood. Here, we propose a novel event-driven taxonomy (EDT)-based text mining tool to prioritize stressors likely to elicit water quality deterioration. The tool integrated automated literature collection, natural language processing using adverse outcome pathway-based toxicological terminologies and machine learning to classify event drivers (EDs). From aquatic toxicity assessments within China over the past decade, we gathered over 14 000 sources of information. With a dictionary that included 1039 toxicological terms, 15 bioassay-related modes of actions were mapped, yet less than half of the bioassays could be elucidated by available adverse outcome pathways. To fill these mechanistic knowledge gaps, we developed a Naive Bayesian ED-classifier to annotate apical responses. The classifier's 4-fold cross-validation reached 74% accuracy and labeled 85% bioassays as 26 EDs. Narcosis, estrogen receptor-, and aryl hydrogen receptor-mediators were the major EDs in aquatic systems across China, whereas individual regions had distinct ED fingerprints. The EDT-based tool provides a promising diagnostic strategy to inform region-specific bioassay design and selection for water quality assessments in a big data era.