.png){kind=icon}
返回
POPs identification using simple low-code machine learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.scitotenv.2024.171143.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Effectively identifying persistent organic pollutants (POPs) with extensive organic chemical datasets poses a formidable challenge but is of utmost importance. Leveraging machine learning techniques can enhance this process, but previous models often demanded advanced programming skills and high-end computing resources. In this study, we harnessed the simplicity of PyCaret, a Python-based package, to construct machine-learning models for POP screening based on 2D molecular descriptors. We compared the performance of these models against a deep convolutional neural network (DCNN) model. Utilising minimal Python code, we generated several models that exhibited superior or comparable performance to the DCNN. The most outstanding performer, the Light Gradient Boosting Machine (LGBM), achieved an accuracy of 96.20 %, an AUC of 97.70 %, and an F1 score of 82.58 %. This model outshone the DCNN model. Furthermore, it excelled in identifying POPs within the REACH PBT and compiled industrial chemical lists. Our findings highlight the accessibility and simplicity of PyCaret, requiring only a few lines of code, rendering it suitable for non-computing professionals in environmental sciences. The ability of low code machine learning tools (e.g. PyCaret) to facilitate model comparison and interpretation holds promise, encouraging prompt assessment and management of chemical substances.
Keyword:
Persistent organic pollutants (POPs)
Classification
Machine learning
PyCaret
Risk assessment
Chemical management
期刊
IF:
8
论文数:
7.1W
被引数:
46.4W
机构
引用论文
.png){kind=icon}
Oxygen vacancies confined in ultrathin nickel oxide nanosheets for enhanced electrocatalytic methanol oxidation氧空位限制在超薄氧化镍纳米片中的增强电催化甲醇氧化
PREAI
分享收藏Enzymatic assays for the assessment of toxic effects of halogenated organic contaminants in water and food. A review用于评估水和食品中卤化有机污染物的毒性作用的酶测定法。一篇评论
OAAI
OAAI分享收藏Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and Regional Chemical Inventories对化学污染的全球理解: 对国家和区域化学清单的首次综合分析OAAI
OAAI分享收藏
查看更多内容