大数据挖掘和机器学习在毒理学中的应用
文献类型：期刊论文
| 作者 | 滕跃发1,3; 王晓晴1,3 ; 李斐2,3; 吴惠丰2,3; 吉成龙2,3; 于进福4
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| 刊名 | 生态毒理学报
 |
| 出版日期 | 2022 |
| 卷号 | 17期号:1页码:93-101 |
| 关键词 | 数据挖掘 机器学习 结构-活性关系 AOP 计算毒理学 |
| ISSN号 | 1673-5897 |
| 其他题名 | Application of Data Mining and Machine Learning in Toxicology |
| 文献子类 | 期刊论文 |
| 英文摘要 | 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. |
| 语种 | 中文 |
| CSCD记录号 | CSCD:7221479 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/34198]  |
| 专题 | 烟台海岸带研究所_中科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_近岸生态与环境实验室 |
| 作者单位 | 1.中国科学院大学,北京100049; 2.中国科学院海洋大科学研究中心,青岛266071 3.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; 4.烟台职业学院网络中心,烟台264670; |
| 推荐引用方式 GB/T 7714 | 滕跃发,王晓晴,李斐,等. 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. |
| APA | 滕跃发,王晓晴,李斐,吴惠丰,吉成龙,&于进福.(2022).大数据挖掘和机器学习在毒理学中的应用.生态毒理学报,17(1),93-101. |
| MLA | 滕跃发,et al."大数据挖掘和机器学习在毒理学中的应用".生态毒理学报 17.1(2022):93-101. |
入库方式： OAI收割
来源：烟台海岸带研究所
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