AIMC Topic: Neural Networks, Computer

Clear Filters Showing 20931 to 20940 of 31376 articles

iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.

BMC bioinformatics
BACKGROUND: Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of ...

DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.

BMC bioinformatics
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering ins...

Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Micron (Oxford, England : 1993)
Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is that of accurate image reconstruction from a limited number of measurements. In this work, we present a...

Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records.

IEEE journal of biomedical and health informatics
Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents a multiple disease risk prediction method to systematically assess future disease risks for patients...

Association extraction from biomedical literature based on representation and transfer learning.

Journal of theoretical biology
Extracting biological relations from biomedical literature can deliver personalized treatment to individual patients based on their genomic profiles. In this paper, we present a novel sentence-level attention-based deep neural network to predict the ...

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction.

Ultrasound in medicine & biology
Neonatal hip ultrasound imaging has been widely used for a few decades in the diagnosis of developmental dysplasia of the hip (DDH). Graf's method of hip ultrasonography is still the most reproducible because of its classification system; yet, the re...

A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, ...

On Clinical Event Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records.

IEEE journal of biomedical and health informatics
Healthcare process leaves patient treatment trajectory (PTT), described as a sequence of interdependent clinical events affiliated with a large volume of longitudinal therapy and treatment information. Predicting the future clinical event in PTT, as ...

A computational model of systems memory consolidation and reconsolidation.

Hippocampus
In the mammalian brain, newly acquired memories depend on the hippocampus (HPC) for maintenance and recall, but over time, the neocortex takes over these functions, rendering memories HPC-independent. The process responsible for this transformation i...

Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture e...