Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach

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Last updated 11 novembro 2024
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach - ScienceDirect
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Frontiers A Case Study on Integrating a New Key Event Into an Existing Adverse Outcome Pathway on Oxidative DNA Damage: Challenges and Approaches in a Data-Rich Area
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities. - Abstract - Europe PMC
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Defining Molecular Initiating Events in the Adverse Outcome Pathway Framework for Risk Assessment
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
High throughput data-based, toxicity pathway-oriented development of a quantitative adverse outcome pathway network linking AHR activation to lung damages - ScienceDirect
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Identification of toxicity pathway of diesel particulate matter using AOP of PPARγ inactivation leading to pulmonary fibrosis - ScienceDirect
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
PPARγ agonists inhibit TGF-β induced pulmonary myofibroblast differentiation and collagen production: implications for therapy of lung fibrosis
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
PDF) Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
PDF) Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale, Particle and Fibre Toxicology
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Toxics, Free Full-Text
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
Frontiers From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to  Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast  Database and a Deep Learning Artificial Neural Network Model-Based Approach
In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities. - Abstract - Europe PMC

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