Novel intelligent Bayesian computing networks for predictive solutions of nonlinear multi-delayed tumor oncolytic virotherapy systems  

作　　者：Nabeela Anwar Iftikhar Ahmad Adiqa Kausar Kiani Muhammad Shoaib Muhammad Asif Zahoor Raja 

机构地区：[1]Department of Mathematics University of Narowal,50600,Pakistan [2]Department of Mathematics University of Gujrat,50700,Pakistan [3]Future Technology Research Center National Yunlin University of Science and Technology 123 University Road,Section 3,Douliou Yunlin 64002,Taiwan,R.O.China [4]Yuan Ze University,AI Center Taoyuan,320,Taiwan,R.O.China

出　　处：《International Journal of Biomathematics》2024年第7期273-310,共38页生物数学学报(英文版)

摘　　要：Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation.Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations.This work aims to design a soft computing approach using stupendous knacks of neural networks(NNs)optimized with Bayesian regularization(BR),i.e.NNs-BR,procedure.The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy(TVT)models in terms of the dynamic interactions between the tumor cells free of viruses,tumor cells infected by viruses,viruses,and cytotoxic T-lymphocytes(CTLs).The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells,virus-free tumor cell clearance rate by CTLs,CTLs clearance rate for infectious tumor cells,the natural lifecycle of infectious tumor cells,the natural lifecycle of viral cell,the natural lifecycle of CTLs cells,tumor cells free of viruses'maximum proliferation rate,production of tumor cells with an infection,CTLs simulated ratio for infectious tumor cells,CTLs simulated ratios for virus-free cells and delay in time.The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model.The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10^(-9) to 10^(-16).The eficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics,learning curves on MSE,and error histograms for the dynamics of TVT model.

关 键 词：Tumor virotherapy model delay differential system Adams numerical approach neural networks soft computing approach Bayesian regularization technique 

分 类 号：R73[医药卫生—肿瘤]