[Retracted] Identifying COVID‐19‐Specific Transcriptomic Biomarkers with Machine Learning Methods

Lei, Chen; Li, Zhandong; Zeng, Tao; Zhang, Yuhang; Kong, Feng; Huang, Tao; Cai, Yu-Dong

doi:10.1155/2021/9939134

Cited by 20 publications

(20 citation statements)

References 82 publications

(110 reference statements)

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“…However, the field of genomics is struggling with the successful implementation of gold standard machine learning (ML) algorithms for clinically proven reproducible computational predictions [ 38 ]. It is necessary to automate the process of gene-variant data annotation, expression and simulation to produce timely presentable results [ 117 ]. Current limitations in this context imply gaps among clinics and fundamental basic and applied research; difficulties in getting exigent approvals and timeliness of data availability; levels of granularity in clinical information; and application of appropriate modeling strategies that allow learning in the data continuum [ 118 ].…”

Section: Discussionmentioning

confidence: 99%

Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19

2022

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Aim: A human immunogenetics variation study was conducted in samples collected from diverse COVID-19 populations. Materials & methods: Whole-genome and whole-exome sequencing (WGS/WES), data processing, analysis and visualization pipeline were applied to identify variants associated with genes of interest. Results: A total of 2886 mutations were found across the entire set of 13 genomes. Functional annotation of the gene variants revealed mutation type and protein change. Many variants were found to be biologically implicated in COVID-19. The involvement of these genes was also found in multiple other diseases. Conclusion: The analysis determined that ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI had functional implications and TMPRSS4 was the gene most altered in virally infected patients.

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Section: Discussionmentioning

confidence: 99%

Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19

2022

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“…The method compares the value of the original features to the significance achievable at random, as indicated by their permuted copies, and gradually removes unnecessary features to stabilize the test. In the last few years, Boruta has been widely used in processing biological data ( Chen et al, 2021a ; Huang et al, 2021a ; Zhou et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…High-throughput sequencing and data analysis provide convenience for understanding the immune cell characteristics of COVID-19 ( Chen et al, 2021a ; Li et al, 2021a ; Stephenson et al, 2021 ; Zhang et al, 2021 ). Based on the single-cell profiling of gene expression and surface proteins of 696,109 peripheral blood immune cells from 102 patients with COVID-19 having different disease severity and 41 control individuals, we used a machine learning statistical analysis to explore the expression characteristics of various immune cells in patients with COVID-19 and immune molecules related to the COVID-19 immunity mechanism.…”

Section: Introductionmentioning

confidence: 99%

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Huang

Liao

et al. 2022

Front. Mol. Biosci.

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Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

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“…Three sets of top 50 feature genes were selected by the three feature selection methods. Thereafter, we set up a classifier to filter optimal feature genes by methods described previously ( 18 ). A random forest classifier was constructed based on the python package “skfeature” ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection

Sun

Zhang

Yang

et al. 2022

Front. Public Health

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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.

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[Retracted] Identifying COVID‐19‐Specific Transcriptomic Biomarkers with Machine Learning Methods

Cited by 20 publications

References 82 publications

Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19

Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection

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