AIMC Topic: Deep Learning

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Bearing fault diagnosis based on Gramian angular field and DenseNet.

Mathematical biosciences and engineering : MBE
Rolling bearings are the core components of mechanical and electrical systems. A practical fault diagnosis scheme is the key to ensure operational safety. There are excessive characteristic parameters with remarkable randomness and severe signal coup...

Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution.

Optics express
Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high...

Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.

Mathematical biosciences and engineering : MBE
Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surroun...

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

Nucleic acids research
De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major...

Adenosine Triphosphate Protects from Elevated Extracellular Calcium-Induced Damage in Human Proximal Kidney Cells: Using Deep Learning to Predict Cytotoxicity.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology
BACKGROUND/AIMS: In kidney, extracellular [Ca] can modulate intracellular [Ca] to control key cellular processes. Hence, extracellular [Ca] is normally maintained within narrow range. We tested effect of extracellular ATP on viability of human proxim...

Deciphering signatures of natural selection via deep learning.

Briefings in bioinformatics
Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning...

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

Briefings in bioinformatics
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple...

A merged molecular representation deep learning method for blood-brain barrier permeability prediction.

Briefings in bioinformatics
The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to p...

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.

Briefings in bioinformatics
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite id...