Tarakeswara Rao B, Lakshmana Kumar V N, Padmapriya D, Pant Kumud, B Tejaswini, Alonazi Wadi B, Almutairi Khalid M A
Department of Computer Science & Engineering, Kallam Haranadhareddy Institute of Technology, Dasaripalem, Andhra Pradesh 522019, India.
Department of Electronics and Communication Engineering, M.V.G.R.College of Engineering (Autonomous), Vizianagaram, Andhra Pradesh 535005, India.
Evid Based Complement Alternat Med. 2022 Jul 14;2022:7639875. doi: 10.1155/2022/7639875. eCollection 2022.
In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.
最近，接触过甲型流感病毒（IAV）的人可能不会出现不良反应。尽管KLRD1已被发现是一种流感易感性生物标志物，但暴露前的宿主基因表达是否能够预测流感症状仍有待观察。在本文中，我们利用深度神经网络，从含有各种亚型病毒的人类基因表达数据集中对流感进行检测。这项研究能够利用这些数据集预测流感的传播，并提供根除流感所需的步骤。进行模拟以测试该模型在针对各种输入数据集预测传播方面的效率。模拟结果表明，与其他现有方法相比，所提出的方法在发现流感传播方面具有更高的预测能力，高出2.98%。
