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发表于 2025-3-21 17:57:40
书目名称Machine Learning and Deep Learning in Computational Toxicology影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0620469<br><br> <br><br>书目名称Machine Learning and Deep Learning in Computational Toxicology读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0620469<br><br> <br><br>
follicle
发表于 2025-3-21 23:40:05
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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发表于 2025-3-22 01:52:21
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Indent
发表于 2025-3-22 04:53:11
978-3-031-20732-7This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright pro
debase
发表于 2025-3-22 09:56:51
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LARK
发表于 2025-3-22 13:54:08
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.
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发表于 2025-3-22 19:56:13
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整理
发表于 2025-3-22 22:51:12
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
发表于 2025-3-23 04:21:31
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.
multiply
发表于 2025-3-23 09:10:38
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.