GERD

Structure-aware protein-protein interaction site prediction using deep graph convolutional network.

MOTIVATION: Protein-protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mo...

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

MOTIVATION: Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a pr...

Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene sign...

Artificial intelligence in musculoskeletal conditions.

Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive ch...

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survi...

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link S...

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural netwo...

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug c...

Stretchable origami robotic arm with omnidirectional bending and twisting.

Inspired by the embodied intelligence observed in octopus arms, we introduce magnetically controlled origami robotic arms based on Kresling patterns f...

Current status and future perspectives of computational studies on human-virus protein-protein interactions.

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-v...

A survey on computational models for predicting protein-protein interactions.

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suf...

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.

Small molecule modulators of protein-protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. W...

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being condu...

Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network.

OBJECTIVE: Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the s...

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.

BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies...

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer.

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and ...

Use of ColonFlag score for prioritisation of endoscopy in colorectal cancer.

OBJECTIVE: Colorectal cancer (CRC) is the fourth most common cancer in UK. Symptomatic patients are referred via an urgent pathway and although most a...

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review.

Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been sh...

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational ...

Automated Detection of Crohn's Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks.

BACKGROUND AND AIMS: Passable intestinal strictures are frequently detected on capsule endoscopy [CE]. Such strictures are a major component of inflam...