Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

doi:10.1111/bcpt.13959

科研成果详情

题名	Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats
作者	Su, Hang 1; Zhao, Dong 1; Heidari, Ali Asghar 2; Cai, Zhennao 3; Chen, Huiling 3; Zhu, Jiayin4
发表日期	2023-11-09
发表期刊	Basic & clinical pharmacology & toxicology 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	Disease diagnosis Extreme learning machine Feature selection Hepatotoxicity Pyrene Swarm intelligence
其他关键词	OPTIMIZATION
摘要	Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover, and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2, and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.
资助项目	Zhejiang Province Public Welfare Technology Application Research Project[LGD22H260004];Science and Technology Plan Project of Wenzhou, China[Y20220050];Natural Science Foundation of Zhejiang Province[LZ22F020005];National Natural Science Foundation of China[U1809209];National Natural Science Foundation of China[62076185]
出版者	WILEY
ISSN	1742-7835
EISSN	1742-7843
卷号	134 期号:2 页码:250-271
DOI	10.1111/bcpt.13959
页数	22
WOS类目	Pharmacology & Pharmacy ; Toxicology
WOS研究方向	Pharmacology & Pharmacy ; Toxicology
WOS记录号	WOS:001113088400001
收录类别	PUBMED ; SCIE ; SCOPUS
URL	查看原文
PubMed ID	37945549
SCOPUSEID	2-s2.0-85178475947
通讯作者地址	[Zhao, Dong]College of Computer Science and Technology,Changchun Normal University,Jilin,Changchun,130032,China ; [Chen, Huiling]College of Computer Science and Artificial Intelligence,Wenzhou University,Zhejiang,Wenzhou,325035,China ; [Zhu, Jiayin]Laboratory Animal Center,Wenzhou Medical University,Zhejiang,Wenzhou,325035,China
Scopus学科分类	Toxicology;Pharmacology
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/183815
专题	实验动物中心
通讯作者	Zhao, Dong; Chen, Huiling; Zhu, Jiayin
作者单位	1.College of Computer Science and Technology,Changchun Normal University,Changchun,China; 2.School of Surveying and Geospatial Engineering,College of Engineering,University of Tehran,Tehran,Iran; 3.Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province,Wenzhou University,Wenzhou,China; 4.Laboratory Animal Center,Wenzhou Medical University,Wenzhou,China
通讯作者单位	实验动物中心
推荐引用方式 GB/T 7714	Su, Hang,Zhao, Dong,Heidari, Ali Asghar,et al. Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats[J]. Basic & clinical pharmacology & toxicology,2023,134(2):250-271.
APA	Su, Hang, Zhao, Dong, Heidari, Ali Asghar, Cai, Zhennao, Chen, Huiling, & Zhu, Jiayin. (2023). Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats. Basic & clinical pharmacology & toxicology, 134(2), 250-271.
MLA	Su, Hang,et al."Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats".Basic & clinical pharmacology & toxicology 134.2(2023):250-271.