AIMC Topic: Neural Networks, Computer

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DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.

International journal of molecular sciences
Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a ...

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

Scientific reports
The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database...

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

Medical science monitor : international medical journal of experimental and clinical research
On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their foundation research on machine learning with artificial neural networks, which resulted in the current app...

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

The bone & joint journal
AIMS: The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular ...

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

Clinical and translational gastroenterology
INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commo...

Unsupervised learning for real-time and continuous gait phase detection.

PloS one
Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cann...

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

PloS one
Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and en...

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

Magnetic resonance in medicine
PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always poss...

A general adaptive unsupervised feature selection with auto-weighting.

Neural networks : the official journal of the International Neural Network Society
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required t...

Data-sampled time-varying formation for singular multi-agent systems with multiple leaders.

Neural networks : the official journal of the International Neural Network Society
The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the comm...