AIMC Topic: Deep Learning

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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Nature communications
Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existi...

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

International journal of paediatric dentistry
BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to prese...

Deep learning-assisted surface-enhanced Raman spectroscopy detection of intracellular reactive oxygen species.

Talanta
Realizing the intelligent analysis of the intracellular reactive oxygen species (ROS) is beneficial to quick diagnosis of diseases. Herein, surface-enhanced Raman spectroscopy (SERS) technology was combined with deep learning to establish a smart det...

Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

Journal of applied clinical medical physics
BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, bu...

Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.

Radiography (London, England : 1995)
INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy...

Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.

Computers in biology and medicine
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of multiple sclerosi...

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Computational biology and chemistry
Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to representing biological sequences and automatically extracting their key features for improving SE identifica...

Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments.

Marine pollution bulletin
Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling...

Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Journal of environmental management
Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. ...

A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.

The journal of physical chemistry. B
Live-cell imaging using fluorescence microscopy enables researchers to study cellular processes in unprecedented detail. These techniques are becoming increasingly popular among microbiologists. The emergence of microfluidics and deep learning has si...