AIMC Topic: Deep Learning

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Accuracy and data efficiency in deep learning models of protein expression.

Nature communications
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for stra...

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase.

PloS one
Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomogr...

Operon Finder: A Deep Learning-based Web Server for Accurate Prediction of Prokaryotic Operons.

Journal of molecular biology
Operons are groups of consecutive genes that transcribe together under the regulation of a common promoter. They influence protein regulation and various physiological pathways, making their accurate detection desirable. The detection of operons thro...

Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants.

STAR protocols
Investigating network behavior from host-pathogen interactions is challenging. Here, we present the deep-learning-based protocol to construct an immune-related gene network and list the genes involved in the defense response of host to specific bioti...

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection proces...

Deep Learning for Image Analysis in Kidney Care.

Advances in kidney disease and health
Analysis of medical images, such as radiological or tissue specimens, is an indispensable part of medical diagnostics. Conventionally done manually, the process may sometimes be time-consuming and prone to interobserver variability. Image classificat...

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning.

International journal of environmental research and public health
The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in...

Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.

Alzheimer's research & therapy
BACKGROUND: Alzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared ...

Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study.

BMC medical informatics and decision making
BACKGROUND: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result f...

A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images.

BMC medical imaging
PURPOSE: To compare a deep learning model with a radiomics model in differentiating high-grade (LR-3, LR-4, LR-5) liver imaging reporting and data system (LI-RADS) liver tumors from low-grade (LR-1, LR-2) LI-RADS tumors based on the contrast-enhanced...