Titlebook: Machine Learning and Deep Learning in Computational Toxicology; Huixiao Hong Book 2023 This is a U.S. government work and not under copyri

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Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dys employed to develop QTTPM models using dyPLIDs. Results indicate that dyPLID-based models outperformed those developed using conventional descriptors in predicting holdout test datasets. The QTTPM identified key dyPLIDs providing insights on ligand-induced protein structural changes that are impor

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Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Riskpling weights. A viable approach for controlling confounding in complex observational surveys could open a new frontier for machine learning models and analysis in toxicological and medication studies with NHANES and other complex survey data.

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Multivariate Curve Resolution for Analysis of Heterogeneous System in Toxicogenomicsivariate curve resolution (MCR) model transfers a mixed system into a bilinear model of pure component contributions, which can be useful in untangling heterogeneous systems such as TGx. In this chapter, the main goal of applying MCR to TGx is to reduce the effect of heterogeneous data on the expres

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Book 2023gy. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students

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隐士

Dmitry Filimonov,Alexander Dmitriev,Anastassia Rudik,Vladimir Poroikov

证实

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哺乳动物

Radiculopathy