Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles
A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/12/9/1443 |
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| author | Tae-June Choi Hyung-Eun An Chang-Bae Kim |
| author_facet | Tae-June Choi Hyung-Eun An Chang-Bae Kim |
| author_sort | Tae-June Choi |
| collection | DOAJ |
| description | A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from <i>Daphnia magna</i>, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems. |
| first_indexed | 2024-03-09T23:22:33Z |
| format | Article |
| id | doaj.art-7ebff107f26d418ab9d763239cf3c52b |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T23:22:33Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-7ebff107f26d418ab9d763239cf3c52b2023-11-23T17:24:14ZengMDPI AGLife2075-17292022-09-01129144310.3390/life12091443Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic ProfilesTae-June Choi0Hyung-Eun An1Chang-Bae Kim2Department of Biotechnology, Sangmyung University, Seoul 03016, KoreaDepartment of Biotechnology, Sangmyung University, Seoul 03016, KoreaDepartment of Biotechnology, Sangmyung University, Seoul 03016, KoreaA wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from <i>Daphnia magna</i>, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.https://www.mdpi.com/2075-1729/12/9/1443environmental monitoringaquatic ecosystemtoxic organic compounds<i>Daphnia magna</i>transcriptomic profilesmachine learning |
| spellingShingle | Tae-June Choi Hyung-Eun An Chang-Bae Kim Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles Life environmental monitoring aquatic ecosystem toxic organic compounds <i>Daphnia magna</i> transcriptomic profiles machine learning |
| title | Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles |
| title_full | Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles |
| title_fullStr | Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles |
| title_full_unstemmed | Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles |
| title_short | Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using <i>Daphnia magna</i> Transcriptomic Profiles |
| title_sort | machine learning models for identification and prediction of toxic organic compounds using i daphnia magna i transcriptomic profiles |
| topic | environmental monitoring aquatic ecosystem toxic organic compounds <i>Daphnia magna</i> transcriptomic profiles machine learning |
| url | https://www.mdpi.com/2075-1729/12/9/1443 |
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