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Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models

文献类型：期刊论文

作者Iftikhar, Sara5; Karim, Asad Mustafa2; Karim, Aoun Murtaza3,4; Karim, Mujahid Aizaz10; Aslam, Muhammad11; Rubab, Fazila1; Malik, Sumera Kausar9; Kwon, Jeong Eun2; Hussain, Imran8; Azhar, Esam I.6,7
刊名JOURNAL OF ENVIRONMENTAL MANAGEMENT
出版日期2023-02-15
卷号328页码:10
关键词Antibiotic resistance genes Machine learning Black box models Explainable Artificial intelligence Recreational beaches
ISSN号0301-4797
DOI10.1016/j.jenvman.2022.116969
英文摘要Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model -agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6 '-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.
WOS关键词SENSITIVITY-ANALYSIS
资助项目Institutional Fund Projects[IFPIP: 1270-141-1442] ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; Ministry of Education
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000900162100006
出版者ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
资助机构Institutional Fund Projects ; Institutional Fund Projects ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; Ministry of Education ; Ministry of Education ; Institutional Fund Projects ; Institutional Fund Projects ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; Ministry of Education ; Ministry of Education ; Institutional Fund Projects ; Institutional Fund Projects ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; Ministry of Education ; Ministry of Education ; Institutional Fund Projects ; Institutional Fund Projects ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; King Abdulaziz University, DSR, Jeddah, Saudi Arabia ; Ministry of Education ; Ministry of Education
源URL[http://ir.iggcas.ac.cn/handle/132A11/106921]  
专题中国科学院地质与地球物理研究所
通讯作者Kang, Se Chan; Yasir, Muhammad
作者单位1.COMSATS Univ Islamabad, Dept Elect & Comp Engn, Wah Campus, Wah Cantt 47040, Pakistan
2.Kyung Hee Univ, Coll Life Sci, Dept Biotechnol, Yongin 17104, South Korea
3.Univ Chinese Acad Sci, Inst Geol & Geophys, Beijing, Peoples R China
4.Univ Punjab, Inst Geol, Lahore 54590, Pakistan
5.Natl Univ Sci & Technol NUST, Dept Elect Engn & Comp Sci, Islamabad 64000, Pakistan
6.King Abdulaziz Univ, Fac Appl Med Sci, Dept Med Lab Sci, Jeddah 21589, Saudi Arabia
7.King Abdulaziz Univ, King Fahd Med Res Ctr, Special Infect Agents Unit, Jeddah 21589, Saudi Arabia
8.Comsats Univ Islamabad, Environm Biotechnol Lab, Dept Biotechnol, Abbottabad Campus, Islamabad, Pakistan
9.Univ Suwon, Dept Biosci & Biotechnol, Hwaseong 18323, South Korea
10.Sheikh Zayed Med Coll Hosp, Rahim Yar Khan, Pakistan
推荐引用方式
GB/T 7714		 Iftikhar, Sara,Karim, Asad Mustafa,Karim, Aoun Murtaza,et al. Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2023,328:10.
APA Iftikhar, Sara.,Karim, Asad Mustafa.,Karim, Aoun Murtaza.,Karim, Mujahid Aizaz.,Aslam, Muhammad.,...&Yasir, Muhammad.(2023).Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models.JOURNAL OF ENVIRONMENTAL MANAGEMENT,328,10.
MLA Iftikhar, Sara,et al."Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models".JOURNAL OF ENVIRONMENTAL MANAGEMENT 328(2023):10.

入库方式： OAI收割

来源：地质与地球物理研究所

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