Name: Machine Learning and Deep Learning in Computational Toxicology (en Inglés)
SKU: 67388648
Price: 169583.00 COP
Availability: InStock
Author: Huixiao Hong
ISBN: 9783031207310

portada Machine Learning and Deep Learning in Computational Toxicology (en Inglés)

Machine Learning and Deep Learning in Computational Toxicology (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Huixiao Hong

Año

2023

Idioma

Inglés

N° páginas

676

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x3.4 cm

ISBN13

9783031207310

Machine Learning and Deep Learning in Computational Toxicology (en Inglés)

Huixiao Hong (Autor) · Springer · Tapa Blanda

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Reseña del libro "Machine Learning and Deep Learning in Computational Toxicology (en Inglés)"

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 in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. 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. 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. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.