Machine Learning and Deep Learning in Computational Toxicology
This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques i...
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| その他の著者: | |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| 版: | 1st ed. 2023. |
| シリーズ: | Computational Methods in Engineering & the Sciences,
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-3-031-20730-3 |
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目次:
- Machine Learning and Deep Learning Promotes Predictive Toxicology for Risk Assessment of Chemicals
- Multi-Modal Deep Learning Approaches for Molecular Toxicity prediction
- Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions
- Drug Effect Deep Learner Based on Graphical Convolutional Network
- AOP Based Machine Learning for Toxicity Prediction
- Graph Kernel Learning for Predictive Toxicity Models
- Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mecha-nistic Knowledge and Real-World Data
- Multitask Learning for Quantitative Structure-Activity Relationships: A Tutorial
- Isalos Predictive Analytics Platform: Cheminformatics, Nanoinformatics and Data Mining Applications
- ED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting Chemicals
- Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated Toxicity
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
- Applicability Domain Characterization for Machine Learning QSAR Models
- Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk. .