AIMC Topic: Deep Learning

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Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1-Deficient Mice.

The Journal of pharmacology and experimental therapeutics
Metabolic flux augmentation via glucose transport activation may be desirable in glucose transporter 1 (Glut1) deficiency syndrome (G1D) and dementia, whereas suppression might prove useful in cancer. Using lung adenocarcinoma cells that predominantl...

Deep learning in regulatory genomics: from identification to design.

Current opinion in biotechnology
Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved rema...

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator.

Sensors (Basel, Switzerland)
Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem...

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Sensors (Basel, Switzerland)
Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that imp...

Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System.

Sensors (Basel, Switzerland)
Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to rai...

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities.

Environmental monitoring and assessment
In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used t...

Identify Hard-to-Place Kidneys for Early Engagement in Accelerated Placement With a Deep Learning Optimization Approach.

Transplantation proceedings
Recommended practices that follow match-run sequences for hard-to-place kidneys succumb to many declines, accruing cold ischemic time and exacerbating kidney quality that may lead to unnecessary kidney discard. Hard-to-place deceased donor kidneys ac...

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features.

Using Deep Learning to Predict Treatment Response in Patients with Hepatocellular Carcinoma Treated with Y90 Radiation Segmentectomy.

Journal of digital imaging
Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose-response threshold, where dose estimates are highly dependent on accurate SPECT/CT acquisition, registration, and reconstruction. ...

Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.

Cancer medicine
BACKGROUND: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of...