利用多层人工神经网络分析冠状病毒刺突基因推断潜在宿主的改进方法

Kamlesh Lakhwani, S. Bhargava, D. Somwanshi, Ruchi Doshi, K. Hiran
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引用次数: 5

摘要

许多冠状病毒能够在物种间传播。近年来,冠状病毒的传播在全球引起了恐慌。因此,推断冠状病毒的潜在宿主具有重要意义。本文分析了从冠状病毒刺突基因中计算出的19个参数来推断冠状病毒的潜在宿主。提出了一种增强的多层神经网络方法来分析数据。将该模型与决策树预测器、支持向量机预测器和PNN预测器等统计预测器进行了比较。所有模型均显示出较高的预测准确率,SVM预测准确率为82.051%,PNN预测准确率为85.256%,决策树预测准确率为94.872%,最高准确率为95。%由所提出的多层感知器预测器表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Enhanced Approach to Infer Potential Host of Coronavirus by Analyzing Its Spike Genes Using Multilayer Artificial Neural Network
Numerous coronaviruses are capable of transmitting interspecies. In recent years, transmission of coronavirus created a panic situation in the whole world. Therefore it is very important to infer the potential host of coro- navirus. In this research work nineteen parameters computed from the spike genes of coronavirus has been analysed to infer the potential host of coron- avirus. An enhanced multilayer neural network approach is proposed to analyse the data. The proposed model is compared with the other exiting statistical predictors like decision tree predictor, Support vector machine predictor and PNN predictor. All the model shown the higher accuracy such as 82.051 % by SVM predictor, 85.256% by PNN predictor,94.872% by decision tree predictor, and the highest accuracy 95.% is shown by proposed Multilayer Perceptron Predictor.
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