AIMC Topic: Deep Learning

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Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting.

Cell systems
Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplet...

Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.

BMC medical imaging
PURPOSE: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.

Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.

Scientific reports
Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact...

Deep learning for fast super-resolution ultrasound microvessel imaging.

Physics in medicine and biology
. Ultrasound localization microscopy (ULM) enables microvascular reconstruction by localizing microbubbles (MBs). Although ULM can obtain microvascular images that are beyond the ultimate resolution of the ultrasound (US) diffraction limit, it requir...

Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy.

The Journal of craniofacial surgery
BACKGROUND: The diagnosis of thyroid-associated ophthalmopathy (TAO) usually requires a comprehensive examination, including clinical symptoms, radiological examinations, and blood tests. Therefore, cost-effective and noninvasive methods for the dete...

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Computers in biology and medicine
Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous exa...

Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.

The journal of physical chemistry. B
The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artif...

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.

Nature communications
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of me...

Deep Learning-Based Synthetic TOF-MRA Generation Using Time-Resolved MRA in Fast Stroke Imaging.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Time-resolved MRA enables collateral evaluation in acute ischemic stroke with large-vessel occlusion; however, a low SNR and spatial resolution impede the diagnosis of vascular occlusion. We developed a CycleGAN-based deep lea...

A deep learning method for drug-target affinity prediction based on sequence interaction information mining.

PeerJ
BACKGROUND: A critical aspect of drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In ...