AIMC Topic: Deep Learning

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Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models.

Journal of chemical information and modeling
Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, ma...

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation.

BMC bioinformatics
BACKGROUND: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible ...

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation.

Computational intelligence and neuroscience
The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people's actual needs to a certain extent. With the assistance of Lightweight Deep Learning (LDL) models, Interacti...

Recommendation of Business Models for Agriculture-Related Platforms Based on Deep Learning.

Computational intelligence and neuroscience
Agriculture is a basic and pillar industry. With the integration and development of Internet+, platform economy, and various industries, the business model of agriculture-related platforms is also constantly innovating. In this context, it is necessa...

Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules.

PloS one
Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification...

Single-shot T mapping via multi-echo-train multiple overlapping-echo detachment planar imaging and multitask deep learning.

Medical physics
BACKGROUND: Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single-shot T ...

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models.

Sensors (Basel, Switzerland)
Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight o...

Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.

BMC bioinformatics
BACKGROUND: Metabolomics is a primary omics topic, which occupies an important position in both clinical applications and basic researches for metabolic signatures and biomarkers. Unfortunately, the relevant studies are challenged by the batch effect...

Deep Learning-Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair.

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PURPOSE: Modern endovascular hybrid operating rooms generate large amounts of medical images during a procedure, which are currently mostly assessed by eye. In this paper, we present fully automatic segmentation of the stent graft on the completion d...

Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR).