AIMC Topic: Deep Learning

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Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Physiology (Bethesda, Md.)
Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, chal...

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Journal of computational chemistry
Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. T...

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

Journal of pharmacokinetics and pharmacodynamics
The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) i...

CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced.

Artificial intelligence in medicine
Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, C...

NeuroQuantify - An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning.

Journal of neuroscience methods
BACKGROUND: The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neur...

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

The international journal of cardiovascular imaging
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elast...

Dual-consistency guidance semi-supervised medical image segmentation with low-level detail feature augmentation.

Computers in biology and medicine
In deep-learning-based medical image segmentation tasks, semi-supervised learning can greatly reduce the dependence of the model on labeled data. However, existing semi-supervised medical image segmentation methods face the challenges of object bound...

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

Computers in biology and medicine
We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the...

Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia.

Reproduction (Cambridge, England)
To enhance surgical testicular sperm retrieval outcome for men with nonobstructive azoospermia, a deep-learning model was developed to identify positive seminiferous tubules by labeling 110 images with sperm-containing tubules sampled during microdis...

Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature.

Chemical research in toxicology
Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the primary cause of acute liver failure. Identifying the scientific literature related to DILI is crucial for monitoring, investigating, and conducting met...