September 22, 2025 article Open access Interpretable machine learning models for predicting hormesis of binary antibiotic mixtures on Vibrio qinghaiensis sp.-Q67 Journal of Hazardous Materials DOI: 10.1016/j.jhazmat.2025.139922 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 1 References 1 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles 2023 Environment International article Materials Science Nanoparticles: synthesis and applications Open access 88 citations 1 result