Related Experiment Video
Updated: Sep 5, 2025
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
Published on: March 1, 2024
Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection
Yanbao Sun1, Qi Zhang2, Qi Yang2
1Department of Radiology, Affiliated Hospital of Jiaxing University, Jiaxing, China.
Insights
Identifying new biomarkers is crucial for accurate Corona Virus Disease 2019 (COVID-19) diagnosis. This study identified 36 feature genes as potential COVID-19 biomarkers using advanced bioinformatics methods.
Area of Science:
- Genomics
- Bioinformatics
- Infectious Diseases
Background:
- The COVID-19 pandemic highlights the need for accurate diagnostic methods.
- Current detection technologies for SARS-CoV-2 are limited by potential false positives and negatives.
- Identifying novel biomarkers is essential to improve COVID-19 test precision.
Purpose of the Study:
- To identify and validate novel gene expression biomarkers for accurate COVID-19 diagnosis.
- To screen feature genes from COVID-19 positive and negative samples.
- To analyze the biological functions and pathways associated with potential biomarkers.
Main Methods:
- Utilized ReliefF, minimal-redundancy-maximum-relevancy, and Boruta_MCFS for initial feature gene screening.
- Employed incremental feature selection with a random forest classifier to select 36 optimal feature genes.
- Performed Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analysis.
- Verified biomarker efficacy using dimensionality reduction analysis.
Main Results:
- Successfully screened and selected 36 optimal feature genes with potential as COVID-19 biomarkers.
- Revealed enriched biological functions and signaling pathways associated with these genes.
- Demonstrated the ability of the 36 selected genes to effectively distinguish between COVID-19 positive and negative samples.
- Confirmed the utility of Boruta_MCFS in identifying robust biomarkers.
Conclusions:
- The 36 feature genes identified, particularly those selected via Boruta_MCFS, show promise as reliable biomarkers for COVID-19.
- These biomarkers can significantly enhance the accuracy and reliability of COVID-19 diagnostic tests.
- Further validation is warranted to integrate these findings into clinical diagnostic practices.
Abstract:
Since the first report of SARS-CoV-2 virus in Wuhan, China in December 2019, a global outbreak of Corona Virus Disease 2019 (COVID-19) pandemic has been aroused. In the prevention of this disease, accurate diagnosis of COVID-19 is the center of the problem. However, due to the limitation of detection technology, the test results are impossible to be totally free from pseudo-positive or -negative. Improving the precision of the test results asks for the identification of more biomarkers for COVID-19. On the basis of the expression data of COVID-19 positive and negative samples, we first screened the feature genes through ReliefF, minimal-redundancy-maximum-relevancy, and Boruta_MCFS methods. Thereafter, 36 optimal feature genes were selected through incremental feature selection method based on the random forest classifier, and the enriched biological functions and signaling pathways were revealed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Also, protein-protein interaction network analysis was performed on these feature genes, and the enriched biological functions and signaling pathways of main submodules were analyzed. In addition, whether these 36 feature genes could effectively distinguish positive samples from the negative ones was verified by dimensionality reduction analysis. According to the results, we inferred that the 36 feature genes selected via Boruta_MCFS could be deemed as biomarkers in COVID-19.
More Related Videos
07:35Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
10:37Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
Published on: November 30, 2016
