Article

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis

2021-02-24

Abstract excerpt

<title>Abstract</title> <p><bold>Background:</bold> Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of definitive clinical diagnostic biomarkers. In present study, we aim to select several candidate gene...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a9dc1f3e-6402-5979-bc26-a70c74cab483
DOI: 10.21203/rs.3.rs-225591/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics AnalysisDOI 10.21203/rs.3.rs-225591/v1Read

Exploring the Diagnosis of Immune-Related Genes in Metabolic Syndrome Based on Three AlgorithmsSemantic proximity Read

WGCNA-Based Identification of Hub Genes and Key Pathways Involved in ObesitySemantic proximity Read

The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysisSemantic proximity Read

RNA sequencing and bioinformatics analysis of tissue biopsy of abdominal fat in obesity associated with cardio-metabolic diseasesSemantic proximity Read

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort studySemantic proximity Read

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes MellitusSemantic proximity Read

Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and ObesitySemantic proximity Read

Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic TargetSemantic proximity Read

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitisSemantic proximity Read

Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass IndexSemantic proximity Read

Machine learning-based identification of key lipid metabolism-related genes and associated ceRNA networks in NAFLDSemantic proximity Read

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learningSemantic proximity Read