GERD

MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction.

BACKGROUND AND OBJECTIVE: Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biol...

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein In...

Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.

INTRODUCTION: Post-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clin...

Effectiveness of FEES with artificial intelligence-assisted computer-aided diagnosis.

OBJECTIVES: FEES is a standard procedure for diagnosing dysphagia. However, appropriate evaluation of FEES findings is difficult for inexperienced eva...

Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.

BACKGROUND: The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional statu...

Development of a hybrid hydrogel for submucosal injection in endoscopic resection of gastrointestinal neoplasm: From laboratory to clinical trial.

Submucosal injection solution is essential for successful endoscopic resection of the early gastrointestinal tumor. We evaluated a new endoscopic hyd...

Hybrid stage 1 palliation with simultaneous off-pump ventricular assist device placement in neonates with high-risk single ventricle anatomy: Initial experience.

BACKGROUND: Infants with single ventricle heart disease and severe atrioventricular valve regurgitation have poor outcomes following conventional stag...

Artificial Intelligence Chatbot Shows Multiple Inaccuracies When Responding to Questions About Eosinophilic Esophagitis.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numer...

Unraveling the complexities of pathological voice through saliency analysis.

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is ...

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) image...

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial in...

A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

BACKGROUND: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep...

Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer.

OBJECTIVE: Transoral surgery (TOS) is a widely used treatment for laryngopharyngeal cancer. There are some difficult cases of setting the extent of re...

Deep learning in endoscopy: the importance of standardisation.

Prediction of Protein-Protein Interactions Using Vision Transformer and Language Model.

The knowledge of protein-protein interaction (PPI) helps us to understand proteins' functions, the causes and growth of several diseases, and can aid ...

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder.

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to pre...

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network.

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to disc...

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper pr...

Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation.

A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated d...