Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9930
Title: SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach
Authors: Pratheeba, J.
Keywords: COVID-19 diagnosis;Feature selection;Transcriptome data;Machine learning models;Differently expressed genes;GO analysis
Issue Date: 2023
Publisher: Springer
Abstract: SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naive Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9930
DOI: https://doi.org/l O. 1007/s42979-023-017036
Appears in Collections:Civil Engineering

Files in This Item:
File Description SizeFormat 
SARS-CoV-2 Diagnosis Using Transcriptome Data A Machine Learning Approach.pdf1.17 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.