Artificial Intelligence in Predictive Toxicology: Identifying influential topics based on science mapping
Abstract views: 55
/ 
PDF downloads: 36
DOI:
https://doi.org/10.61705/yn6y2c83Keywords:
Predictive Toxicology, Artificial Intelligence (AI), Science MappingAbstract
This research paper delves into the transformative landscape of Predictive Toxicology, marked by the integration of Artificial Intelligence (AI) and cutting-edge technologies. The emergence of Predictive Toxicology, driven by computational models leveraging machine learning, signifies a departure from traditional methods, promising accelerated risk assessment and enhanced comprehension of chemical-biological interactions. Through science mapping, this study explores the interconnected web of literature in this dynamic field, aiming to trace its historical evolution, identify influential research hubs, and discern collaborative networks. The dataset analysis spanning 1993 to 2023 unveils trends, emphasizing the surge in AI's prominence and the sustained relevance of foundational topics. This study not only offers a comprehensive overview but also provides a roadmap for future research in the intersection of AI and Predictive Toxicology.
References
Cronin MTD, Belfield SJ, Briggs KA, Enoch SJ, Firman JW, Frericks M, et al. Making in silico predictive models for toxicology FAIR. Vol. 140, Regulatory toxicology and pharmacology : RTP. 2023. p. 105385.
Ewart L, Apostolou A, Briggs SA, Carman CV, Chaff JT, Heng AR, et al. Author Correction: Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology. Vol. 3, Communications medicine. 2023. p. 7.
Romano JD, Hao Y, Moore JH, Penning TM. Automating Predictive Toxicology Using ComptoxAI. Vol. 35, Chemical research in toxicology. 2022. p. 1370–82.
Fitzpatrick SC. Predictive toxicology for regulatory decisions: Implementing new approaches at US Food and Drug Administration. Vol. 63, Toxicology in vitro : an international journal published in association with BIBRA. 2020. p. 104659.
Paini A, Campia I, Cronin MTD, Asturiol D, Ceriani L, Exner TE, et al. Towards a qAOP framework for predictive toxicology - Linking data to decisions. Vol. 21, Computational toxicology (Amsterdam, Netherlands). 2022. p. 100195.
Knudsen TB, Fitzpatrick SC, Abrew KND, Birnbaum LS, Chappelle A, Daston GP, et al. FutureTox IV Workshop Summary: Predictive Toxicology for Healthy Children. Vol. 180, Toxicological sciences : an official journal of the Society of Toxicology. 2021. p. 198–211.
Svingen T. Endocrine Disruptors in a New Era of Predictive Toxicology and Dealing With the “More is Different” Challenge. Vol. 4, Frontiers in toxicology. 2022. p. 900479.
Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Vol. 34, Chemical research in toxicology. 2021. p. 217–39.
Giorgini M, Taroncher M, Ruiz MJ, Rodríguez-Carrasco Y, Tolosa J. In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites. Vol. 13, Brain sciences. 2023.
Ewart L, Apostolou A, Briggs SA, Carman CV, Chaff JT, Heng AR, et al. Author Correction: Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology. Vol. 3, Communications medicine. 2023. p. 16.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Medical Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJMR open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
