Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.

利用基因组学数据中的深度学习 (DL) 方法在癌症预测方面取得了重大进展。过去几年基因表达数据集的持续可用性使其成为最容易获取的全基因组数据来源之一，推动了癌症生物信息学研究和癌症基因组数据的高级预测。为了对这一主题做出贡献，所提出的工作基于使用从策划微阵列数据库 (CuMiDa) 获得的基因表达数据，在卷积神经网络 (CNN) 和循环神经网络 (RNN) 中对脑癌的五类进行深度学习预测。该数据库用于癌症分类，可在 CuMiDa 官方网站上公开访问。本文使用一维卷积神经网络 (1D-CNN) 和带有或不带有贝叶斯超参数优化 (BO) 的 RNN 分类器来实现深度学习方法。 (BO + 1D-CNN + RNN) 的混合模型组合的准确率达到了 100% 的最高分类准确率，而不是先前工作中 ML 模型的 95% 和 (1D-CNN + RNN) 算法的 90%论文中考虑到。因此，根据混合模型（BO + 1D-CNN + RNN）对脑癌基因表达进行分类，可以为不同类型脑癌患者提供更准确、更有用的评估。因此，基因表达数据被用来创建基于深度学习分类的混合模型，该模型将在脑癌的治疗中发挥重要作用。