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

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nucleosides, nucleotides & nucleic acids - 1 Jan 2023

Ülger Yakup, Delik Anıl

Abstract excerpt

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to Carcinoma, Hepatocellular

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Insulin Resistance

Share to Liver Neoplasms

Share to Neural Networks, Computer

Share to Non-alcoholic Fatty Liver Disease

Share to Polymorphism, Single Nucleotide

Source

Identifiers

PMID: 36448439
DOI: 10.1080/15257770.2022.2152046

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.PMID 36448439Read ↗

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysisSemantic proximity Read ↗

Machine learning-based mortality prediction models for non-alcoholic fatty liver disease in the general United States populationSemantic proximity Read ↗

Effect of PNPLA3 polymorphism on diagnostic performance of various noninvasive markers for diagnosing and staging nonalcoholic fatty liver disease.Semantic proximity Read ↗

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severitySemantic proximity Read ↗

Machine Learning Soft Voting Algorithm for Prediction and Detection of Nonalcoholic Fatty Liver DiseaseSemantic proximity Read ↗

Application of Attention and Graph Transformer-Based Approaches for RNA Biomarker Discovery in Metabolically-Associated Fatty Liver Disease (MAFL/NASH)Semantic proximity Read ↗

Machine Learning Enables Single-Score Assessment of MASLD Presence and SeveritySemantic proximity Read ↗

A machine-learning approach for nonalcoholic steatohepatitis susceptibility estimation.Semantic proximity Read ↗

ADVANCED ARTIFICIAL INTELLIGENCE ENABLED METHODS FOR EARLY DETECTION OF NON-ALCOHOLIC FATTY LIVER DISEASE AND ASSOCIATED HEALTH RISKSSemantic proximity Read ↗

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosisSemantic proximity Read ↗

New Discriminant Method for Identifying the Aggressive Disease Phenotype of Non-alcoholic Fatty Liver Disease.Semantic proximity Read ↗

ANN Prediction of Metabolic Syndrome: a Complex Puzzle that will be Completed.Semantic proximity Read ↗

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