ArticleFrontiers in public health2022
XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.Clinical and experimental dental research · 2025Pooled it
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Integrating AI with PCR for Tuberculosis Diagnosis: Evaluating a Deep Learning Model for Chest X-Rays.Bioengineering (Basel, Switzerland) · 2025Article
- Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data.BMC public health · 2025Article
- Evaluating antimicrobial resistance and clinical outcomes in surgical ICU using a machine learning perspective: a retrospective observational study.BMC infectious diseases · 2025Observational
- The moderating role of professional grief in the relationship between job stress dimensions and turnover intention among nurses.BMC psychology · 2025Article
- Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.Scientific reports · 2025Observational
- Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.Microbial biotechnology · 2025Article
- Potential Biomarkers for Predicting the Risk of Developing Into Long COVID After COVID-19 Infection.Immunity, inflammation and disease · 2025Article
- Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.PloS one · 2025Article
- Risk prediction model for pneumothorax or pleural effusion after microwave ablation in patients with lung malignancy.Heliyon · 2024Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.Interdisciplinary sciences, computational life sciences · 2024Article
- Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage.PloS one · 2024Article
- Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia.Journal of translational medicine · 2023Article
- Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology.International journal of molecular sciences · 2023Article
- Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.International journal of molecular sciences · 2023Article
- Diagnosis of Acute Aortic Syndromes on Non-Contrast CT Images with Radiomics-Based Machine Learning.Biology · 2023Article
- Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss.Frontiers in oncology · 2023Article
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Abstract
In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.
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