文献类型：期刊文献

英文题名：Pharmacological Effects of Trastuzumab and Multi-model Breast Cancer Analysis based on Artificial Neural Network

作者：Zhang Wei[1];Wang Yongping

第一作者：张伟

通信作者：Zhang, W[1]

机构：[1]Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China; Gansu Univ Chinese Med, Affiliated Hosp, Lanzhou 730000, Gansu, Peoples R China

第一机构：甘肃中医药大学第二附属医院

通信机构：[1]Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2019

卷号：59

期号：6

起止页码：80

外文期刊名：BOLETIN DE MALARIOLOGIA Y SALUD AMBIENTAL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000489574200013)】；

基金：This paper was supported by Science Innovation Fund of Gansu University of Chinese Medicine(KCYB2018-6).

语种：英文

外文关键词：mRNA; Breast cancer; Artificial neural network; Trastuzumab

摘要：Invasive breast cancer mainly refers to a malignant tumor whose cells have penetrated breast ducts or lobular acinar basement membrane and invaded stroma. Almost 80% of invasive breast cancer is adenocarcinomas, mainly originating from breast parenchymal epithelial cells. In this paper, the author analyse the pharmacological effects of trastuzumab and multi-model breast cancer analysis based on artificial neural network. Trastuzumab is a human monoclonal antibody that binds to the adjacent membrane of extracellular HER2 and inhibits the proliferation and survival of HER2-dependent cancer cells. At the same time, this paper studies whether the sparsity of adaptive kernel learning correlation vector machine can improve the execution efficiency of the algorithm, which is of great significance for the practical application of the mass detection method. In future studies, artificial neural network model can be established by increasing the follow-up time, appropriately increasing the number of samples, and inputting more factors, so as to further improve the scientific and accuracy of the research results.