Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

In contemporary society, the incidence of depression is increasing significantly around the world. At present, most of the treatment methods for depression are psychological counseling and drug therapy. However, this approach does not allow patients to visualize the logic of hormones at the patholog...

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Bibliographic Details
Published in:Frontiers in big data 2023-09, Vol.6, p.1268503-1268503
Main Authors: Wang, Xuyue, Yu, Wangyang, Zhang, Chao, Wang, Jia, Hao, Fei, Li, Jin, Zhang, Jing
Format: Article
Language:English
Subjects:
Big Data
data technologies
healthcare
intelligent computing
Petri nets
visualization
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Summary:In contemporary society, the incidence of depression is increasing significantly around the world. At present, most of the treatment methods for depression are psychological counseling and drug therapy. However, this approach does not allow patients to visualize the logic of hormones at the pathological level. In order to better apply intelligence computing methods to the medical field, and to more easily analyze the relationship between norepinephrine and dopamine in depression, it is necessary to build an interpretable graphical model to analyze this relationship which is of great significance to help discover new treatment ideas and potential drug targets. Petri net (PN) is a mathematical and graphic tool used to simulate and study complex system processes. This article utilizes PN to study the relationship between norepinephrine and dopamine in depression. We use PN to model the relationship between the norepinephrine and dopamine, and then use the invariant method of PN to verify and analyze it. The mathematical model proposed in this article can explain the complex pathogenesis of depression and visualize the process of intracellular hormone-induced state changes. Finally, the experiment result suggests that our method provides some possible research directions and approaches for the development of antidepressant drugs.
ISSN:2624-909X
2624-909X
DOI:10.3389/fdata.2023.1268503