AIMC Topic: Deep Learning

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Evaluating water resource carrying capacity using the deep learning method: a case study of Yunnan, Southwest China.

Environmental science and pollution research international
Water resource carrying capacity (WRCC) is an important index for measuring the relations between water resource systems and socio-economic-environmental development. In view of the difficulty in describing the complex and nonlinear relationships bet...

Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Journal of chemical information and modeling
Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic data sets with hidden bias, which will limit the accurac...

Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.

The journal of physical chemistry. A
Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict t...

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.

Journal of chemical theory and computation
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations...

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Sensors (Basel, Switzerland)
Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patien...

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach.

Sensors (Basel, Switzerland)
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring ...

Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device.

Sensors (Basel, Switzerland)
The importance of an embedded wearable device with automatic detection and alarming cannot be overstated, given that 15-30% of patients with atrial fibrillation are reported to be asymptomatic. These asymptomatic patients do not seek medical care, he...

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Sensors (Basel, Switzerland)
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using method...

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Molecules (Basel, Switzerland)
Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer...

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

Scientific reports
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The...