Improving the diagnostic accuracy using amplification and sequencing of the SARS-CoV-2 genome

ABSTRACT

Deep learning involves using deep neural networks with multiple hierarchical hidden layers of nonlinear processing of input to allow complex patterns to be discovered from vast volumes of raw data. Performance is improved through adjusting, optimizing, and regulating hyperparameters. An unsupervised study can find patterns in the data when there are no labels or distribution of probability in data. The study of genomic sequencing and genome expression is typically characterized by deep learning. Prediction of genomic profiles is based on around 1000 programs of the NIH Integrated Network (LINCS) that have dramatically surpassed linear regression in both RNA-seq findings and microarrays in terms of predictive precision. To predict the transcription factor binding sites, inputs taken from Deep CNN have been encrypted. By retrieving higher levels from those in raw nucleotides, the deeper model would make categorization more accurate. Genetic variations may affect the transcription of DNA and mRNA. © 2022 Elsevier Inc. All rights reserved.