Results 11 to 20 of about 1,723,253 (280)
, 2018 This volume explores techniques that are currently used to understand solid target-specific models in computational toxicology. The chapters are divided into four sections and discuss topics such as molecular descriptors, QSAR and read-across; molecular ...
S. Thakkar, R. Perkins, H. Hong, W. Tong
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Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials [PDF]
International Journal of Molecular Sciences, 2017 As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold ...
Guangchao Chen +2 more
exaly +4 more sources
The digital evolution in toxicology: pioneering computational education for emerging challenges
BMC Medical EducationThe educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools.
Jin Wang, Jianxiang Li
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Computational toxicology using the OpenTox application programming interface and Bioclipse [PDF]
BMC Research Notes, 2011 Background Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of ...
Willighagen Egon L +4 more
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Proteomics for systems toxicology
Computational and Structural Biotechnology Journal, 2014 Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment.
Bjoern Titz +7 more
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Computational Systems Toxicology
, 2015 Toxicogenomics is an emerging field defined by the adaptation and application of functional genomics techniques to toxicology. Recent advances in generating and analyzing multi-omics data have facilitated the development of toxicogenomics to provide novel answers to many toxicology-related questions.
Constantinescu, Simona +4 more
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Machine Learning Methods in Computational Toxicology [PDF]
, 2018 Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology.
openaire +3 more sources
The rapid development of computational toxicology. [PDF]
Arch Toxicol, 2020 Bolt HM, Hengstler JG.
europepmc +4 more sources
Toxics, 2022 With the abundance of chemicals in the environment that could potentially cause neurodevelopmental deficits, there is a need for rapid testing and chemical screening assays.
Kimberly A. Jarema +6 more
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Application of Artificial Intelligence and Machine Learning in Computational Toxicology in Aquatic Toxicology [PDF]
PollutionComputational toxicology is a rapidly growing field that utilizes artificial intelligence (AI) and machine learning (ML) to predict the toxicity of chemical compounds.
Mahdi Banaee +2 more
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