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滕跃发, 王晓晴, 李斐, 吴惠丰, 吉成龙, 于进福. 大数据挖掘和机器学习在毒理学中的应用[J]. 生态毒理学报, 2022, 17(1): 93-101. doi: 10.7524/AJE.1673-5897.20210915001
引用本文: 滕跃发, 王晓晴, 李斐, 吴惠丰, 吉成龙, 于进福. 大数据挖掘和机器学习在毒理学中的应用[J]. 生态毒理学报, 2022, 17(1): 93-101. doi: 10.7524/AJE.1673-5897.20210915001
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Teng Yuefa, Wang Xiaoqing, Li Fei, Wu Huifeng, Ji Chenglong, Yu Jinfu. Application of Data Mining and Machine Learning in Toxicology[J]. Asian Journal of Ecotoxicology, 2022, 17(1): 93-101. doi: 10.7524/AJE.1673-5897.20210915001
Citation: Teng Yuefa, Wang Xiaoqing, Li Fei, Wu Huifeng, Ji Chenglong, Yu Jinfu. Application of Data Mining and Machine Learning in Toxicology[J]. Asian Journal of Ecotoxicology, 2022, 17(1): 93-101. doi: 10.7524/AJE.1673-5897.20210915001
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大数据挖掘和机器学习在毒理学中的应用

  • 1. 中国科学院海岸带环境过程与生态修复重点实验室(烟台海岸带研究所), 山东省海岸带环境过程重点实验室, 中国科学院烟台海岸带研究所, 烟台 264003;

  • 2. 烟台职业学院网络中心, 烟台 264670;

  • 3. 中国科学院大学, 北京 100049;

  • 4. 中国科学院海洋大科学研究中心, 青岛 266071

    • 作者简介: 滕跃发(1998-),男,硕士研究生,研究方向为生态毒理学,E-mail:yfteng@yic.ac.cn
    通讯作者: 李斐, E-mail: fli@yic.ac.cn ;  于进福, E-mail: yujinfu@ytvc.edu.cn
  • 基金项目:

    烟台市科技创新发展计划项目(2020MSGY060)

    国家自然科学基金资助项目(21677173,41530642)

    中国科学院青年创新促进会项目(2017255)

  • 中图分类号: X171.5

Application of Data Mining and Machine Learning in Toxicology

  • 1. CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences;Shandong Key Laboratory of Coastal Environmental Processes;Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China;

  • 2. Network Centre, Yantai Vocational College, Yantai 264670, China;

  • 3. University of Chinese Academy of Sciences, Beijing 100049, China;

  • 4. Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China

    • Corresponding authors: Li Fei, fli@yic.ac.cn ;  Yu Jinfu, yujinfu@ytvc.edu.cn
  • Fund Project:

  • 摘要: 随着高通量筛选技术的快速发展，化学品的毒性相关信息与日俱增。现今快速发展的数据挖掘技术和机器学习等计算机方法为化学品的毒性预测和风险防控提供了新途径。有害结局路径(adverse outcome pathway,AOP)将化合物的结构、分子启动事件和生物的有害结局建立关联，为污染物的毒性测试、预测和评估提供了新的模式，最终实现风险评估并应用于管理决策。定量结构-活性关系(QSAR)建模、分子模拟以及多组学技术在AOP的各个方面发挥了重要作用。基于此，本综述主要介绍数据挖掘与机器学习在毒理学中的应用方法，涉及QSAR建模、分子模拟及组学等方面，并结合实例分析系统阐述了当前研究的重点与方向，以更好地适应当前大数据时代的研究背景。
    Abstract: With the rapid development of high-throughput screening technologies, information on the toxicity of chemicals is growing day by day. The rapid development of computerized methods, such as data mining and machine learning, has provided a new approach to the toxicity prediction and risk control of chemicals. It is very important to establish the framework of ecological risk assessment by integrating a series of effective tools. Among these tools, adverse outcome pathway (AOP) can connect the structure of compounds, molecular initiation events, and adverse effects of organisms, thus can be used for risk assessment and management decisions. Quantitative structure-activity relationship (QSAR) modeling, molecular simulation and multi-omics techniques play important roles in the function of AOP. This review mainly introduces the application methods of data mining and machine learning in toxicology, including QSAR modeling, molecular simulation and omics. The current research focus and direction of computational toxicology were also reviewed with the aim of the better understanding of the big data era.
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  • 收稿日期:  2021-09-15

大数据挖掘和机器学习在毒理学中的应用

    通讯作者: 李斐, E-mail: fli@yic.ac.cn ;  于进福, E-mail: yujinfu@ytvc.edu.cn
    作者简介: 滕跃发(1998-),男,硕士研究生,研究方向为生态毒理学,E-mail:yfteng@yic.ac.cn
  • 1. 中国科学院海岸带环境过程与生态修复重点实验室(烟台海岸带研究所), 山东省海岸带环境过程重点实验室, 中国科学院烟台海岸带研究所, 烟台 264003;
  • 2. 烟台职业学院网络中心, 烟台 264670;
  • 3. 中国科学院大学, 北京 100049;
  • 4. 中国科学院海洋大科学研究中心, 青岛 266071
  • 收稿日期:  2021-09-15
基金项目:

烟台市科技创新发展计划项目(2020MSGY060)

国家自然科学基金资助项目(21677173,41530642)

中国科学院青年创新促进会项目(2017255)

摘要: 随着高通量筛选技术的快速发展，化学品的毒性相关信息与日俱增。现今快速发展的数据挖掘技术和机器学习等计算机方法为化学品的毒性预测和风险防控提供了新途径。有害结局路径(adverse outcome pathway,AOP)将化合物的结构、分子启动事件和生物的有害结局建立关联，为污染物的毒性测试、预测和评估提供了新的模式，最终实现风险评估并应用于管理决策。定量结构-活性关系(QSAR)建模、分子模拟以及多组学技术在AOP的各个方面发挥了重要作用。基于此，本综述主要介绍数据挖掘与机器学习在毒理学中的应用方法，涉及QSAR建模、分子模拟及组学等方面，并结合实例分析系统阐述了当前研究的重点与方向，以更好地适应当前大数据时代的研究背景。

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