大数据挖掘和机器学习在毒理学中的应用

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室

	大数据挖掘和机器学习在毒理学中的应用
	滕跃发 1,3; 王晓晴 1,3; 李斐 1,4; 吴惠丰 1,4; 吉成龙 1,4; 于进福 2
刊名	生态毒理学报
	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
内容类型	期刊论文
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
作者单位	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
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.