Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 10 of 10 for “"Predictive toxicology"”.
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Machine learning in predictive toxicology: investigating developmental and reproductive toxicity with transfer learning
… of new products or drugs. In the field of predictive toxicology, with animal testing being phased out in some sectors, there is an urgent need for alternative methods for determining toxicity. An *in silico* method such as machine learning is one such popular choice given that the results …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes methodologies for …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes methodologies for …
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Contributions to Ensembles of Models for Predictive Toxicology Applications. On the Representation, Comparison and Combination of Models in Ensembles.
… palette of types and representation formats for predictive models. Managing the models then becomes a big challenge, as well as reusing the models and keeping the consistency of model and data repositories. Sustainable access and quality assessment of these models become limited to researchers. …
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A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques
… to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied …
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A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques
… to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied …
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Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation.
… This research addresses number of issues in predictive toxicology. One issue is the problem of data quality. Although large amount of toxicity data is available from online sources, this data may contain some unreliable samples and may be defined as of low quality. Its presentation also might …
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Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation
… This research addresses number of issues in predictive toxicology. One issue is the problem of data quality. Although large amount of toxicity data is available from online sources, this data may contain some unreliable samples and may be defined as of low quality. Its presentation also might …
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Using Cell Painting and Chemical Data for Small-molecule Bioactivity and Toxicity Prediction
… mechanism of action, new therapeutics, and toxicology predictions. The most popular among them is the Cell Painting assay, has been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. …
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Understanding compound-induced histopathology in rat liver using gene expression network methods
Current drug discovery is a lengthy and costly pipeline; it takes between twelve and fifteen years and costs $1-2 billion (USD). As such, any compound failures represent a sunk cost – exacerbated if such failures occur later in the pipeline. Compound and drug induced liver injury is a significant …