AIMC Topic: Deep Learning

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[Three-Dimensional Reconstruction Technique and Its Application of Binocular Endoscopic Images Based on Deep Learning].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
The clinical application of binocular endoscope relies primarily on the visual system of physicians to create a three-dimensional effect, but it cannot provide accurate depth information. The utilization of 3D reconstruction technology in binocular e...

GeOKG: geometry-aware knowledge graph embedding for Gene Ontology and genes.

Bioinformatics (Oxford, England)
MOTIVATION: Leveraging deep learning for the representation learning of Gene Ontology (GO) and Gene Ontology Annotation (GOA) holds significant promise for enhancing downstream biological tasks such as protein-protein interaction prediction. Prior ap...

Lit-OTAR framework for extracting biological evidences from literature.

Bioinformatics (Oxford, England)
SUMMARY: The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionize drug discovery by extracting evidence from scientific literature for drug target identification and valid...

Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.

Journal of radiation research
This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results on whole slide images (WSIs), and explore the clinical significance of obtained features. This study...

[A lightweight classification network for single-lead atrial fibrillation based on depthwise separable convolution and attention mechanism].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To design a deep learning model that balances model complexity and performance to enable its integration into wearable ECG monitoring devices for automated diagnosis of atrial fibrillation.

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Nucleic acids research
Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant proteins often requires codon optimization. Publicly available optimization tools do not always result...

Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.

Cell systems
The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understan...

A deep learning lightweight model for real-time captive macaque facial recognition based on an improved YOLOX model.

Zoological research
Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare. However, reliably identifying individual macaques in group environments remains a significant challenge. This study introd...

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.

Neural computation
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation....

A deep learning tissue classifier based on differential co-expression genes predicts the pregnancy outcomes of cattle†.

Biology of reproduction
Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artifi...