Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity

Michael Aschner; Robin Mesnage; Anca Oana Docea; Monica Maria Bastos Paoliello; Aristides Tsatsakis; Georgios Giannakakis; Georgios Z. Papadakis; Silvio Roberto Vinceti; Abel Santamaria; Anatoly V. Skalny; Alexey A. Tinkov

doi:10.1016/j.neuro.2021.12.007

Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity

Michael Aschner, Robin Mesnage, Anca Oana Docea, Monica Maria Bastos Paoliello, Aristides Tsatsakis, Georgios Giannakakis, Georgios Z. Papadakis, Silvio Roberto Vinceti, Abel Santamaria, Anatoly V. Skalny, Alexey A. Tinkov

Molecular Pharmacology

Research output: Contribution to journal › Comment/debate › peer-review

3 Scopus citations

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.

Original language	English (US)
Pages (from-to)	9-11
Number of pages	3
Journal	Neurotoxicology
Volume	89
DOIs	https://doi.org/10.1016/j.neuro.2021.12.007
State	Published - Mar 2022

Keywords

Artificial intelligence
Commentary
Neurotoxicity

ASJC Scopus subject areas

Neuroscience(all)
Toxicology

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10.1016/j.neuro.2021.12.007

Cite this

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title = "Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity",

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.",

keywords = "Artificial intelligence, Commentary, Neurotoxicity",

author = "Michael Aschner and Robin Mesnage and Docea, {Anca Oana} and Paoliello, {Monica Maria Bastos} and Aristides Tsatsakis and Georgios Giannakakis and Papadakis, {Georgios Z.} and Vinceti, {Silvio Roberto} and Abel Santamaria and Skalny, {Anatoly V.} and Tinkov, {Alexey A.}",

note = "Funding Information: MA was supported in part by a grant from NIEHS R01ES07331 . Publisher Copyright: {\textcopyright} 2021",

year = "2022",

month = mar,

doi = "10.1016/j.neuro.2021.12.007",

language = "English (US)",

volume = "89",

pages = "9--11",

journal = "Neurotoxicology",

issn = "0161-813X",

publisher = "Elsevier",

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AU - Aschner, Michael

AU - Mesnage, Robin

AU - Docea, Anca Oana

AU - Paoliello, Monica Maria Bastos

AU - Tsatsakis, Aristides

AU - Giannakakis, Georgios

AU - Papadakis, Georgios Z.

AU - Vinceti, Silvio Roberto

AU - Santamaria, Abel

AU - Skalny, Anatoly V.

AU - Tinkov, Alexey A.

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Commentary

KW - Neurotoxicity

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ER -

Leveraging artificial intelligence to advance the understanding of chemical neurotoxicity

Abstract

Keywords

ASJC Scopus subject areas

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