Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art
Само за регистроване кориснике
2022
Аутори
Pantić, IgorPaunović, Jovana
Pejić, Snežana
Drakulić, Dunja R.
Todorović, Ana
Stanković, Sanja
Vučević, Danijela
Cumic, Jelena
Radosavljević, Tatjana
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of dise...ases associated with oxidative damage.
Кључне речи:
Machine learning / Oxidative damage / Reactive oxygen species / Signal analysis / ToxicityИзвор:
Chemico-Biological Interactions, 2022, 358, 109888-Финансирање / пројекти:
DOI: 10.1016/j.cbi.2022.109888
ISSN: 0009-2797
PubMed: 35296431
WoS: 00079131700000
Scopus: 2-s2.0-85126518159
Колекције
Институција/група
VinčaTY - JOUR AU - Pantić, Igor AU - Paunović, Jovana AU - Pejić, Snežana AU - Drakulić, Dunja R. AU - Todorović, Ana AU - Stanković, Sanja AU - Vučević, Danijela AU - Cumic, Jelena AU - Radosavljević, Tatjana PY - 2022 UR - https://vinar.vin.bg.ac.rs/handle/123456789/10212 AB - Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage. T2 - Chemico-Biological Interactions T2 - Chemico-Biological Interactions T1 - Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art VL - 358 SP - 109888 DO - 10.1016/j.cbi.2022.109888 ER -
@article{ author = "Pantić, Igor and Paunović, Jovana and Pejić, Snežana and Drakulić, Dunja R. and Todorović, Ana and Stanković, Sanja and Vučević, Danijela and Cumic, Jelena and Radosavljević, Tatjana", year = "2022", abstract = "Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.", journal = "Chemico-Biological Interactions, Chemico-Biological Interactions", title = "Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art", volume = "358", pages = "109888", doi = "10.1016/j.cbi.2022.109888" }
Pantić, I., Paunović, J., Pejić, S., Drakulić, D. R., Todorović, A., Stanković, S., Vučević, D., Cumic, J.,& Radosavljević, T.. (2022). Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art. in Chemico-Biological Interactions, 358, 109888. https://doi.org/10.1016/j.cbi.2022.109888
Pantić I, Paunović J, Pejić S, Drakulić DR, Todorović A, Stanković S, Vučević D, Cumic J, Radosavljević T. Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art. in Chemico-Biological Interactions. 2022;358:109888. doi:10.1016/j.cbi.2022.109888 .
Pantić, Igor, Paunović, Jovana, Pejić, Snežana, Drakulić, Dunja R., Todorović, Ana, Stanković, Sanja, Vučević, Danijela, Cumic, Jelena, Radosavljević, Tatjana, "Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art" in Chemico-Biological Interactions, 358 (2022):109888, https://doi.org/10.1016/j.cbi.2022.109888 . .