GERD

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 is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted...

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 of mRNA and cell growth. Detection of protein complexes from protein-protein interaction (PPI) networks is the first step for the analysis of biological processes and pathways. Here, we propose a new framework by incorporating Gene Ontology (GO), amino acid background frequency (AABF) and data from...

[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 a variety of new perspectives for endoscopy, such...

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 to study the occurrence and development of glioma,...

Localization strategies for robotic endoscopic capsules: a review.

INTRODUCTION: Nowadays, mass screening campaigns for colorectal cancer diagnosis in the early and curable stage is essential yet limited due to many r...

Formulation and Stability Study of Omeprazole Oral Liquid Suspension for Pediatric Patients.

To develop and to study the physicochemical and microbiological stability of omeprazole liquid oral formulations used as therapeutic agent in many ac...

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 remains a crucial step in systematically understandi...

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. However, wet-lab experiments for detecting SLs are fa...

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 artificial neural network (ANN) model for prediction of h...

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 techniques are videostroboscopy and lately high-speed vid...

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. Typically, these images suffer from low contrast, complex...

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 for colorectal cancer screening in large populatio...

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-...

Artificial intelligence and colonoscopy: Current status and future perspectives.

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endos...

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, especially if amino acid sequences are the only informa...

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

With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attent...

Spotting malignancies from gastric endoscopic images using deep learning.

BACKGROUND: Gastric cancer is a common kind of malignancies, with yearly occurrences exceeding one million worldwide in 2017. Typically, ulcerous and ...

Early esophageal adenocarcinoma detection using deep learning methods.

PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically i...