AIMC Topic: Deep Learning

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A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart recons...

Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.

Biomolecules
Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed...

Pattern recognition of topologically associating domains using deep learning.

BMC bioinformatics
BACKGROUND: Recent increasing evidence indicates that three-dimensional chromosome structure plays an important role in genomic function. Topologically associating domains (TADs) are self-interacting regions that have been shown to be a chromosomal s...

Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy.

eLife
Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale gen...

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily...

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bi...

New Labeling Methods for Deep Learning Real-Valued Inter-Residue Distance Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction-a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorit...

Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses.

EPRI sparse reconstruction method based on deep learning.

Magnetic resonance imaging
Electron paramagnetic resonance imaging (EPRI) is an advanced tumor oxygen concentration imaging method. Now, the bottleneck problem of EPRI is that the scanning time is too long. Sparse reconstruction is an effective and fast imaging method, which m...

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweezers Raman spectroscopy (LTRS) has confirmed to be a powerful tool for studying Bacillus spores at a s...