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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書目名稱Machine Learning and Deep Learning in Computational Toxicology影響因子(影響力)




書目名稱Machine Learning and Deep Learning in Computational Toxicology影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Deep Learning in Computational Toxicology網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Deep Learning in Computational Toxicology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Deep Learning in Computational Toxicology被引頻次




書目名稱Machine Learning and Deep Learning in Computational Toxicology被引頻次學(xué)科排名




書目名稱Machine Learning and Deep Learning in Computational Toxicology年度引用




書目名稱Machine Learning and Deep Learning in Computational Toxicology年度引用學(xué)科排名




書目名稱Machine Learning and Deep Learning in Computational Toxicology讀者反饋




書目名稱Machine Learning and Deep Learning in Computational Toxicology讀者反饋學(xué)科排名

follicle

Huixiao HongCovers comprehensive view of the machine learning and deep learning algorithms, methods, and software tools.Provides many practical applications of machine learning and deep learning techniques in pre

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Indent

978-3-031-20732-7This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright pro

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LARK

Machine Learning and Deep Learning Promote Computational Toxicology for Risk Assessment of Chemicalical reasoning from the human eye and linear experiments to artificial intelligence will improve computational toxicology for risk assessment by unearthing novel discoveries through making unexpected connections across data types, datasets, and toxicology disciplines.

小淡水魚

整理

2662-4869 ions of machine learning and deep learning techniques in preThis 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 stat

Cholagogue

Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolismal effects. Herein, we propose the concept of integral toxicity that concomitantly reflects the overall biological activity of a pharmaceutical substance and its metabolites. The current possibilities and limitations of the multifaceted computational assessment of xenobiotics toxicity are discussed.

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Drug Effect Deep Learner Based on Graphical Convolutional Networkation of the drug. We found that DDEP can predict drug efficacy with accuracy far better than that achieved by simple drug/target classification, and the vector representations grasp well the comprehensive states of a cell.