A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)
Quantitative structure–activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application of machine learning (ML) algorithms has emerged a...
Saved in:
| Main Authors: | Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024007487 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Impact of the use of azole fungicides, other than as human medicines, on the development of azole‐resistant Aspergillus spp.
by: European Food Safety Authority (EFSA), et al.
Published: (2025-01-01) -
Azole fungicides: Potential endocrine disrupting effects and impact on placental steroidogenesis via inhibiting human and rat 3β-hydroxysteroid dehydrogenase
by: Jingyun Yan, et al.
Published: (2025-07-01) -
<i>Aspergillus fumigatus</i> in the Food Production Chain and Azole Resistance: A Growing Concern for Consumers
by: Katherin Castro-Ríos, et al.
Published: (2025-03-01) -
Induced selection of tebuconazole-resistant Aspergillus flavus isolates during germination of treated corn seeds
by: Chiara Morena, et al.
Published: (2025-03-01) -
Novel Azole-Modified Porphyrins for Mitochondria-Targeted Photodynamic Therapy
by: Sabarinathan Rangasamy, et al.
Published: (2025-06-01)