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

Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategies

5 Mar 2023

Abstract excerpt

The prevalence of non-alcoholic fatty liver disease (NAFLD) and NAFLD-associated hepatocellular carcinoma (HCC) has continuously increased in recent years. Machine learning is an effective method for screening the feature genes of a disease for prediction, prevention and personalized treatment. Here, we used the "limma" package and weighted gene co-expression network analysis (WGCNA) to screen 219 NAFLD-related...

Read the complete abstract on PubMed

Topics

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

Share to Cancer, Lipids, and Metabolism
Share to Ferroptosis and cancer prognosis
Share to Liver Disease Diagnosis and Treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 36898287
DOI: 10.1016/j.compbiomed.2023.106724

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

Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategiesPMID 36898287Read ↗

Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysisSemantic 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 ↗

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning.Semantic proximity Read ↗

Identification of diagnostic gene signatures and molecular mechanisms for non-alcoholic fatty liver disease and Alzheimer’s disease through machine learning algorithmsSemantic proximity Read ↗

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver diseaseSemantic proximity Read ↗

An novel effective and safe model for the diagnosis of nonalcoholic fatty liver disease in China: gene excavations, clinical validations, and mechanism elucidationSemantic proximity Read ↗

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

Identification of Key Genes in Non-Alcoholic Fatty Liver Disease Development through Bioinformatics AnalysisSemantic proximity Read ↗

Functional investigation and two-sample Mendelian randomization study of non-alcoholic fatty liver disease hub genes obtained by WGCNASemantic proximity Read ↗

Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing dataSemantic proximity Read ↗

Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learningSemantic proximity Read ↗

Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targetsSemantic proximity Read ↗

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