Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia, Dhaka, Bangladesh.
School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, United States of America.
PLoS One. 2022 Sep 15;17(9):e0274538. doi: 10.1371/journal.pone.0274538. eCollection 2022.
The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.
严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 大流行的破坏性影响几乎使全球经济停滞不前，感染率超过 5.24 亿，导致 600 多万人死亡。最初，人们对 SARS-CoV-2 病毒持重大保留意见，怀疑其感染了蝙蝠，并与蝙蝠密切相关。然而，在学习和对实验证据进行批判性开发的过程中，发现它与动物与人传播过程中鉴定的几个基因簇和病毒蛋白有一些相似之处。尽管有大量的证据和经验教训，但对于 SARS-CoV-2 基因组中病毒生命周期的假定 microRNAs(miRNAs)的探索有限。在这种情况下，本文提出了一种 SARS-CoV-2 前体-miRNAs(pre-miRNAs)的检测方法，有助于快速识别特定的核糖核酸(rnas)。该方法采用人工神经网络，并提出了一个估计准确率为 98.24%的模型。该采样技术包括随机选择高度不平衡的数据集，以在应用包括准确率曲线、损失曲线和混淆矩阵的注册人工神经网络后减少类别不平衡。然后将经典的机器学习方法与该模型及其性能进行比较。该方法有助于识别 RNA 的靶区，并更好地识别 SARS-CoV-2 基因组序列，从而针对病毒的遗传结构设计基于寡核苷酸的药物。
