Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Zhao, Ying; Zhang, Xueer; Zhang, Zhendong; Huang, Wenbo; Tang, Min; Du, Guizhen; Qin, Yufeng

doi:10.1016/j.scitotenv.2024.173420

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2024.173420

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Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Ying Zhao,

Xueer Zhang,

Zhendong Zhang

et al.

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2024

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Cited by 3 publications

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Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Zhang,

Tian,

Liang

et al. 2024

Toxicology

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Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Zhang,

Tian,

Liang

et al. 2024

Toxicology

View full text Add to dashboard Cite

New revolution for quality control of TCM in industry 4.0: Focus on artificial intelligence and bioinformatics

Li,

Fan,

Cheng

et al. 2024

TrAC Trends in Analytical Chemistry

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Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms

Wyatt,

Davis,

Wiegers

et al. 2024

Front. Toxicol.

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In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as “CGPD-tetramers,” wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum.

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Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Cited by 3 publications

References 47 publications

Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

New revolution for quality control of TCM in industry 4.0: Focus on artificial intelligence and bioinformatics

Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms

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