Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

22 Jun 2022

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to COVID-19 Clinical Research Studies
Share to COVID-19 diagnosis using AI
Share to SARS-CoV-2 and COVID-19 Research

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35812523
DOI: 10.3389/fpubh.2022.926069

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic MarkersPMID 35812523Read ↗

Using Machine Learning of Clinical Data to Diagnose COVID-19Semantic proximity Read ↗

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine LearningSemantic proximity Read ↗

PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients.Semantic proximity Read ↗

Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning MethodsSemantic proximity Read ↗

A New Prediction Data Model of High-Risk COVID-19 Patients with Smart Notification (HRCP-SN) Using a Hybridized AlgorithmSemantic proximity Read ↗

A prediction model based on machine learning for diagnosing the early COVID-19 patientsSemantic proximity Read ↗

Multi-Objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection using Medical Image DataSemantic proximity Read ↗

The Feature Extraction for Coronavirus Disease Classification TechniquesSemantic proximity Read ↗

A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifierSemantic proximity Read ↗

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19Semantic proximity Read ↗

A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic AnalysisSemantic proximity Read ↗

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning methodSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.