气候变化领域集成服务门户(CCPortal): A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

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DOI	10.1016/j.ecoinf.2024.102482
	A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors
	Kang, Seung-Ho; Jeong, In-Seon; Lim, Hyeong-Seok
发表日期	2024
ISSN	1574-9541
EISSN	1878-0512
起始页码	80
卷号	80
英文摘要	Caenorhabditis elegans is a representative organism whose DNA structure has been fully elucidated. It has been used as a model organism for various analyses, including genetic functional analysis, individual behavioral analysis, and group behavioral analysis. Recently, it has also been studied as an important bioindicator of water pollution. In previous studies, traditional machine learning methods, such as the Hidden Markov Model (HMM), were used to determine water pollution and identify pollutants based on the differences in the swimming behavior of C. elegans before and after exposure to chemicals. However, these traditional machine learning models have low accuracy and a relatively high false-negative rate. This study proposes a method for detecting water pollution and identifying the types of pollutants using the Long Short-Term Memory (LSTM) model, a deep learning model suitable for time-series data analysis. The swimming activities of C. elegans in each of the image frames are characterized by the Branch Length Similarity (BLS) entropy profile. These BLS entropy profiles are converted into input vectors through additional preprocessing using two clustering methods. We conduct experiments using formaldehyde and benzene at 0.1 mg/L each, with observation time intervals varying from 30 to 180 s. The performance of the proposed method is compared with that of the previously proposed HMM approach and variants of LSTM models, such as Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM).
英文关键词	Caenorhabditis elegans; Biomonitoring system; Water pollution; Water pollutant; Long short-term memory model; Branch length similarity entropy
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001173914900001
来源期刊	ECOLOGICAL INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298380
作者单位	Dongshin University; Chonnam National University
推荐引用方式 GB/T 7714	Kang, Seung-Ho,Jeong, In-Seon,Lim, Hyeong-Seok. A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors[J],2024,80.
APA	Kang, Seung-Ho,Jeong, In-Seon,&Lim, Hyeong-Seok.(2024).A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors.ECOLOGICAL INFORMATICS,80.
MLA	Kang, Seung-Ho,et al."A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors".ECOLOGICAL INFORMATICS 80(2024).