Microarray data analysis is a major challenging field of research in recent days. Machine learning–based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning techniques to mitigate these types of problems. Due the presence of more number of hidden layers, it can easily handle the big amount of data. We have presented a method of classification to understand the convergence of training deep neural network (DNN). The assumptions are taken as the inputs do not degenerate and the network is over-parameterized. Also the number of hidden neurons is sufficiently large. Authors in this piece of work have used DNN for classifying the gene expressions data. The dataset used in the work contains the bone marrow expressions of 72 leukemia patients. A five-layer DNN classifier is designed for classifying acute lymphocyte (ALL) and acute myelocytic (AML) samples. The network is trained with 80% data and rest 20% data is considered for validation purpose. Proposed DNN classifier is providing a satisfactory result as compared to other classifiers. Two types of leukemia are classified with 98.2% accuracy, 96.59% sensitivity, and 97.9% specificity. The different types of computer-aided analyses of genes can be helpful to genetic and virology researchers as well in future generation.

微阵列数据分析是近年来一个主要具有挑战性的研究领域。基于机器学习的自动化基因数据分类是诊断与基因相关的任何故障和疾病的重要方面。由于数据量非常大，因此设计一个合适的能够处理大量数据的分类器至关重要。深度学习是缓解此类问题的先进机器学习技术之一。由于存在更多数量的隐藏层，它可以轻松处理大量数据。我们提出了一种分类方法来理解训练深度神经网络（DNN）的收敛性。这些假设是因为输入不会退化并且网络过度参数化。隐藏神经元的数量也足够大。这项工作的作者使用 DNN 对基因表达数据进行分类。这项工作中使用的数据集包含 72 名白血病患者的骨髓表达。五层 DNN 分类器设计用于对急性淋巴细胞 (ALL) 和急性骨髓细胞 (AML) 样本进行分类。该网络使用 80% 的数据进行训练，其余 20% 的数据用于验证目的。与其他分类器相比，所提出的 DNN 分类器提供了令人满意的结果。两种类型的白血病的分类准确度为 98.2%，敏感性为 96.59%，特异性为 97.9%。不同类型的计算机辅助基因分析对后代的遗传和病毒学研究人员也有帮助。