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A.M. Al-Fakih, Z.Y. Algamal & M.K. Qasim. (2022) An improved opposition-based crow search algorithm for biodegradable material classification. SAR and QSAR in Environmental Research 33:5, pages 403-415.
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F. Lunghini, G. Marcou, P. Gantzer, P. Azam, D. Horvath, E. Van Miert & A. Varnek. (2020) Modelling of ready biodegradability based on combined public and industrial data sources. SAR and QSAR in Environmental Research 31:3, pages 171-186.
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R. Geerts, C. G. van Ginkel & C. M. Plugge. (2017) Read-across of ready biodegradability based on the substrate specificity of N-alkyl polypropylene polyamine-degrading microorganisms. SAR and QSAR in Environmental Research 28:4, pages 311-323.
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Articles from other publishers (12)
Haoqiang Wang, Zejun Huang, Shang Lou, Weihua Li, Guixia Liu & Yun Tang. (2024)
In Silico
Prediction of Skin Sensitization for Compounds
via
Flexible Evidence Combination Based on Machine Learning and Dempster–Shafer Theory
. Chemical Research in Toxicology.
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Tanyaradzwa R. Ngara, Peiji Zeng & Houjin Zhang. (2022) mibPOPdb: An online database for microbial biodegradation of persistent organic pollutants. iMeta 1:4.
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Kuan Huang & Huichun Zhang. (2022) Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water. Environmental Science & Technology 56:17, pages 12755-12764.
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Aaron D. Redman, Jens Bietz, John W. Davis, Delina Lyon, Erin Maloney, Amelie Ott, Jens C. Otte, Frédéric Palais, John R. Parsons & Neil Wang. (2021) Moving persistence assessments into the 21st century: A role for weight‐of‐evidence and overall persistence. Integrated Environmental Assessment and Management 18:4, pages 868-887.
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Myeonghun Lee & Kyoungmin Min. (2022) A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network. ACS Omega 7:4, pages 3649-3655.
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Suvita Rani Sharma, Birmohan Singh & Manpreet Kaur. (2021) Hybrid SFO and TLBO optimization for biodegradable classification. Soft Computing 25:24, pages 15417-15443.
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Stefanie Lorenz, Ann-Kathrin Amsel, Neele Puhlmann, Marco Reich, Oliver Olsson & Klaus Kümmerer. (2021)
Toward Application and Implementation of
in Silico
Tools and Workflows within Benign by Design Approaches
. ACS Sustainable Chemistry & Engineering 9:37, pages 12461-12475.
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Alaa M. Elsayad, Ahmed M. Nassef, Mujahed Al-Dhaifallah & Khaled A. Elsayad. (2020) Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees. International Journal of Environmental Research and Public Health 17:24, pages 9322.
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Cecile Valsecchi, Francesca Grisoni, Viviana Consonni & Davide Ballabio. (2020) Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study. Journal of Chemical Information and Modeling 60:3, pages 1215-1223.
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María Virginia Sabando, Ignacio Ponzoni & Axel J. Soto. (2019) Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Applied Soft Computing 85, pages 105777.
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Francesca Grisoni, Viviana Consonni & Davide Ballabio. (2019) Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project. Journal of Chemical Information and Modeling 59:5, pages 1839-1848.
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Siyang Tian, Yannick Djoumbou-Feunang, Russell Greiner & David S. Wishart. (2018) CypReact: A Software Tool for in Silico Reactant Prediction for Human Cytochrome P450 Enzymes. Journal of Chemical Information and Modeling 58:6, pages 1282-1291.
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