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利用人工智能推进化学神经毒性的研究。

Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity.

机构信息

Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, United States.

Gene Expression and Therapy Group, King's College London, Faculty of Life Sciences & Medicine, Department of Medical and Molecular Genetics, Guy's Hospital, London, SE1 9RT, UK.

出版信息

Neurotoxicology. 2022 Mar;89:9-11. doi: 10.1016/j.neuro.2021.12.007. Epub 2021 Dec 27.

DOI:10.1016/j.neuro.2021.12.007
PMID:34968636
Abstract

Neurotoxicology is a specialty that aims to understand and explain the impact of chemicals, xenobiotics and physical conditions on nervous system function throughout the life span. Herein, we point to the need for integration of novel translational bioinformatics and chemo-informatics approaches, such as machine learning (ML) and artificial intelligence (AI) to the discipline. Specifically, we advance the notion that AI and ML will be helpful in identifying neurotoxic signatures, provide reliable data in predicting neurotoxicity in the context of genetic variability, and improve the understanding of neurotoxic outcomes associated with exposures to mixtures, to name a few.

摘要

神经毒理学是一门旨在了解和解释化学物质、外源性物质和物理条件对整个生命周期内神经系统功能影响的专业。在此,我们指出需要将新的转化生物信息学和化学信息学方法(如机器学习(ML)和人工智能(AI))整合到该学科中。具体来说,我们提出了这样一种观点,即 AI 和 ML 将有助于识别神经毒性特征,在遗传变异的情况下提供可靠的神经毒性预测数据,并提高对与混合物暴露相关的神经毒性结果的理解,仅举几例。

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引用本文的文献

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Editorial: Rising stars in neurotoxicology: 2021.社论:神经毒理学领域的后起之秀:2021年。
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Critical review and analysis of literature on low dose exposure to chemical mixtures in mammalian systems.哺乳动物系统中低剂量化学混合物暴露的文献的批判性回顾和分析。
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Machine Learning and Artificial Intelligence in Toxicological Sciences.机器学习和人工智能在毒理学科学中的应用。
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