Machine learning of synaptic structure with neurons to promote tumor growth

作     者：Erhui WANG Xuelan ZHANG Liancun ZHENG Chang SHU Erhui WANG;Xuelan ZHANG;Liancun ZHENG;Chang SHU

作者机构：School of Energy and Environmental EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083China State Key Laboratory of Cardiovascular DiseaseCenter of Vascular SurgeryFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100037China 

出 版 物：《Applied Mathematics and Mechanics(English Edition)》 (应用数学和力学（英文版）)

年 卷 期：2020年第41卷第11期

页      面：1697-1706页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 100214[医学-肿瘤学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Project supported by the National Natural Science Foundation of China(Nos.11772046 and 81870345) 

主　　题：machine learning technique computational hemodynamics electrodiffusive activity complex synaptic dynamics 

摘      要：In this paper,we use machine learning techniques to form a cancer cell model that displays the growth and promotion of synaptic and electrical ***,such a technique can be applied directly to the spiking neural network of cancer cell *** results show that machine learning techniques for the spiked network of cancer cell synapses have the powerful function of neuron models and potential supervisors for different *** changes in the neural activity of tumor microenvironment caused by synaptic and electrical signals are *** can be used to cancer cells and tumor training processes of neural networks to reproduce complex spatiotemporal dynamics and to mechanize the association of excitatory synaptic structures which are between tumors and neurons in the brain with complex human health behaviors.