Machine Learning and Deep Learning in Computational Toxicology
Language: English Publication details: Springer Cham Switzerland 2023Description: 653 pISBN:- 9783031207297

Current library | Home library | Call number | Materials specified | Status | Date due | Barcode | |
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IMSc Library | IMSc Library | 681.3 HONG (Browse shelf(Opens below)) | Available | 77454 |
Machine Learning and Deep Learning Promotes Predictive Toxicology for Risk Assessment of ChemicalsMulti-Modal Deep Learning Approaches for Molecular Toxicity predictionEmerging Machine Learning Techniques in Predicting Adverse Drug ReactionsDrug Effect Deep Learner Based on Graphical Convolutional NetworkAOP Based Machine Learning for Toxicity Prediction Graph Kernel Learning for Predictive Toxicity ModelsOptimize and Strengthen Machine Learning Models Based on in vitro Assays with Mecha-nistic Knowledge and Real-World DataMultitask Learning for Quantitative Structure-Activity Relationships: A TutorialIsalos Predictive Analytics Platform: Cheminformatics, Nanoinformatics and Data Mining ApplicationsED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting ChemicalsQuantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated ToxicityMold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of ChemicalsApplicability Domain Characterization for Machine Learning QSAR Models Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk
One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology.
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