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

Frontiers in Big Data

Big Data

Chao Zhang¹  Fei Hao²  Wangyang Yu²  Xuyue Wang²  Jing Zhang²  Jin Li²  Jia Wang³ 

[1] Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou, China;Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Shaanxi Normal University, Xi'An, China;School of Computer Science, Shaanxi Normal University, Xi'An, China;School of Information Construction and Management Department, Shaanxi Normal University, Xi'An, China;

关键词: Petri nets;    intelligent computing;    healthcare;    visualization;    data technologies;   

DOI  :  10.3389/fdata.2023.1268503

 received in 2023-08-08, accepted in 2023-08-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Yu, Zhang, Wang, Hao, Li and Zhang.