Our knowledge of genomic medicine has increased at astonishing pace in recent years. As a result of advances in genomics, proteomics and molecular pathology, many candidate biomarkers with potential clinical value have been identified. Furthermore, recent refinements of experimental technique including array-based assays which generate huge datasets and find numerous potentially useful molecules are becoming widespread. The main aim of this research is to develop and validate an intelligent modelling system for the analysis of large array datasets in a nonlinear manner in order to diagnose and predict the bladder cancer disease. In this work, an investigation of epigenetic data for the diagnosis and progression of UCC was performed using intelligent firefly system, based on various clinicopathological criteria of CpG and involving tumour behaviour. An introduction of the most common artificial intelligent-based modelling techniques and gene expression data predictive modelling will be carried out. Experimental work based on MATLAB neural network toolbox will be described and discussed. A new module based on ANN analyse a large array datasets of human genes which can predict the diagnosis and progression of cancer bladder and help to discover the genes causing cancer. Experimental data was collected using intelligent firefly system and tested with artificial neural network technique.

近年来，我们对基因组医学的了解以惊人的速度增长。由于基因组学、蛋白质组学和分子病理学的进步，已经确定了许多具有潜在临床价值的候选生物标志物。此外，最近对实验技术的改进，包括基于阵列的分析，这些分析生成巨大的数据集并发现许多潜在有用的分子，这正变得越来越普遍。本研究的主要目的是开发和验证一种智能建模系统，用于以非线性方式分析大型阵列数据集，以诊断和预测膀胱癌疾病。在这项工作中，基于 CpG 的各种临床病理学标准并涉及肿瘤行为，使用智能萤火虫系统对用于 UCC 诊断和进展的表观遗传数据进行了调查。将介绍最常见的基于人工智能的建模技术和基因表达数据预测建模。将描述和讨论基于 MATLAB 神经网络工具箱的实验工作。基于人工神经网络的新模块分析了人类基因的大型阵列数据集，可以预测膀胱癌的诊断和进展，并有助于发现导致癌症的基因。使用智能萤火虫系统收集实验数据，并使用人工神经网络技术进行测试。基于人工神经网络的新模块分析了人类基因的大型阵列数据集，可以预测膀胱癌的诊断和进展，并有助于发现导致癌症的基因。使用智能萤火虫系统收集实验数据，并使用人工神经网络技术进行测试。基于人工神经网络的新模块分析了人类基因的大型阵列数据集，可以预测膀胱癌的诊断和进展，并有助于发现导致癌症的基因。使用智能萤火虫系统收集实验数据，并使用人工神经网络技术进行测试。