AIMC Topic: Deep Learning

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Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. How...

DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning.

Analytical chemistry
Proteolytic digestion of proteins by one or more proteases is a key step in shotgun proteomics, in which the proteolytic products, i.e., peptides, are taken as the surrogates of their parent proteins for further qualitative or quantitative analysis. ...

Assessing the potential for deep learning and computer vision to identify bumble bee species from images.

Scientific reports
Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and compute...

Development of machine learning model for diagnostic disease prediction based on laboratory tests.

Scientific reports
The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ...

Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography.

Scientific reports
Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation i...

Real-time coronary artery stenosis detection based on modern neural networks.

Scientific reports
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may fac...

Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.

Scientific reports
Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distanc...

Clinical validation of a commercially available deep learning software for synthetic CT generation for brain.

Radiation oncology (London, England)
BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images gene...

Low-N protein engineering with data-efficient deep learning.

Nature methods
Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce...

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning.

Computational and mathematical methods in medicine
Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, th...