AIMC Topic: Deep Learning

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Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning.

International journal of molecular sciences
Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing ...

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset.

Scientific reports
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surge...

Rapid estimation of patient-specific organ doses using a deep learning network.

Medical physics
BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on ti...

iQDeep: an integrated web server for protein scoring using multiscale deep learning models.

Journal of molecular biology
The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment c...

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

Medical physics
BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

The Lancet. Digital health
BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional ...

Cue: a deep-learning framework for structural variant discovery and genotyping.

Nature methods
Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, w...

CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.

BMC bioinformatics
BACKGROUND: Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Sin...

A myoelectric digital twin for fast and realistic modelling in deep learning.

Nature communications
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and ...

Investigation of radiomics and deep convolutional neural networks approaches for glioma grading.

Biomedical physics & engineering express
To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader validation sets.Seven public datasets were considered: (1) low-grade glioma or high-grade glioma (369 p...