GERD

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins.

Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for p...

DSSGNN-PPI: A Protein-Protein Interactions prediction model based on Double Structure and Sequence graph neural networks.

The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein-Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel prediction model for PPIs named DSSGNN-PPI (Double Structure and Sequence GNN for PPIs) is designed. Initially, a distance graph between amino acid res...

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. ...

Automated Endoscopic Diagnosis in IBD: The Emerging Role of Artificial Intelligence.

The emerging role of artificial intelligence (AI) in automated endoscopic diagnosis represents a significant advancement in managing inflammatory bowe...

Deep Learning Based Cystoscopy Image Enhancement.

Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to asse...

Protein-Protein Interfaces: A Graph Neural Network Approach.

Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecu...

Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.

Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparatio...

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 1...

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market.

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. B...

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) cha...

Clinical evaluation of a machine learning-based dysphagia risk prediction tool.

PURPOSE: The rise of digitization promotes the development of screening and decision support tools. We sought to validate the results from a machine l...

Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease.

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificia...

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression....

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) b...

Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms.

Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). While autophagy plays a critical and intricate role ...

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide...

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for un...

The 2023 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board.

Using a systematic literature search of original articles published during 2023 in Gastrointestinal Endoscopy (GIE) and other high-impact medical and ...

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics.

Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent ...

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevert...