Liver Transplantation

Urinary biomarkers of kidney transplant rejection.

PURPOSE OF REVIEW: Despite the introduction of many new immunosuppressive medications, allograft rej...

Enlightened prognosis: Hepatitis prediction with an explainable machine learning approach.

Hepatitis is a widespread inflammatory condition of the liver, presenting a formidable global health...

Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence.

With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become i...

A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern glo...

Machine Learning Models predicting Decompensation in Cirrhosis.

BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention ...

Identification of CACNB1 protein as an actionable therapeutic target for hepatocellular carcinoma via metabolic dysfunction analysis in liver diseases: An integrated bioinformatics and machine learning approach for precise therapy.

In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive...

Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.

BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associa...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C ...

Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.

The classification of Hepatitis C virus (HCV) NS3 inhibitors is essential for identifying potential ...

Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.

BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine le...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative ...

Factor enhanced DeepSurv: A deep learning approach for predicting survival probabilities in cirrhosis data.

BACKGROUND: Over the years, various models, including both traditional and machine learning models, ...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack o...

A predictive study on HCV using automated machine learning models.

Hepatitis C virus (HCV) infection represents a significant contributor to chronic liver disease on a...

AI-Cirrhosis-ECG (ACE) score for predicting decompensation and liver outcomes.

BACKGROUND & AIMS: Accurate prediction of disease severity and prognosis are challenging in patients...

FibrAIm - The machine learning approach to identify the early stage of liver fibrosis and steatosis.

BACKGROUND: Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for...

Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.

Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatoc...

Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.

BACKGROUND: Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). ...

Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions.

Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenge...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%,...

A Machine Learning Model Score Predicts Advanced Fibrosis and Cirrhosis in Indeterminate FIB-4 Patients.