ArticleBioMed research international2021
Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.Current gene therapy · 2025Article
- Identification of key gene expression associated with quality of life after recovery from COVID-19.Medical & biological engineering & computing · 2024Article
- Intracellular peptides in SARS-CoV-2-infected patients.iScience · 2023Article
- Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach.Biochemical genetics · 2023Article
- Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.Pathology, research and practice · 2023Article
- Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.BioMed research international · 2023Article
- SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach.SN computer science · 2023Article
- Retracted: Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.BioMed research international · 2023Article
- An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.Frontiers in public health · 2022Article
- Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods.Frontiers in oncology · 2022Article
- Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.Frontiers in bioengineering and biotechnology · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.BioMed research international · 2022Article
- Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.Frontiers in molecular biosciences · 2022Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.