Peptic Ulcer Disease

Automated feature engineering improves prediction of protein-protein interactions.

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein i...

Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.

IMPORTANCE: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may i...

Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, t...

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in .

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy ...

Protein complex identification based on weighted PPI network with multi-source information.

Proteins form complexes to accomplish biological functions such as transcription of DNA, translation...

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up...

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks.

BACKGROUND: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts ...

Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current ris...

Machine-Learning-Based Predictor of Human-Bacteria Protein-Protein Interactions by Incorporating Comprehensive Host-Network Properties.

The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria re...

Artificial intelligence and the future of endoscopy.

SLMF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. How...

Quantification of apixaban in human plasma using ultra performance liquid chromatography coupled with tandem mass spectrometry.

Apixaban, an inhibitor of direct factor Xa, is used for the treatment of venous thromboembolic event...

An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.

OBJECTIVE: This study was designed to assess clinical predictors of hypoxemia and develop an artific...

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.

Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniq...

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typical...

Applications of artificial intelligence to endoscopy practice: The view from Japan Digestive Disease Week 2018.

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique ...

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

The biomedical literature provides a rich source of knowledge such as protein-protein interactions (...

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, espec...

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective.

With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastr...

Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding.

BACKGROUND: Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have ...