Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Summary: vSPACE is a web-based application presenting a spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, for one or multiple genes at a time, revealing patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis.
Availability And Implementation: vSPACE is implemented in Python Dash as a web-based toolbox designed for data visualization of zonal gene expression patterns in articular cartilage chondrocytes. This tool is freely accessible at: https://vspace.cse.uconn.edu/The source code and extra materials for this service can be downloaded from: https://github.com/zhacheny/vSPACE.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483105 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btae568 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients.
J Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Large-margin Softmax loss using synthetic virtual class.
Neural Netw
September 2025
School of Cyberspace Security (School of Cryptology), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, Hainan, China. Electronic address:
The primary challenge of large-margin learning lies in designing classifiers with strong discriminative power. Although existing large margin methods have achieved success in various classification tasks, they often suffer from weak task generalization and imbalanced handling of easy and hard samples. In this paper, we propose a margin adaptive synthetic virtual Softmax loss (SV-Softmax), which dynamically generates virtual prototypes by synthesizing embedded features and their corresponding prototypes.
View Article and Find Full Text PDFEnhancing Automated Seizure Detection via Self-Calibrating Spatial-Temporal EEG Features with SC-LSTM.
IEEE J Biomed Health Inform
September 2025
Epilepsy, a highly individualized neurological disorder, affects millions globally. Electroencephalography (EEG) remains the cornerstone for seizure diagnosis, yet manual interpretation is labor-intensive and often unreliable due to the complexity of multi-channel, high-dimensional data. Traditional machine learning models often struggle with overfitting and fail in fully capturing the highdimensional, temporal dynamics of EEG signals, restricting their clinical utility.
View Article and Find Full Text PDFLateralised modulation of posterior alpha oscillations by closed loop auditory stimulation during memory retention.
Cereb Cortex
August 2025
School of Psychology, University of Surrey, Stag Hill, Guildford, Surrey, GU2 7XH, United Kingdom.
Alpha oscillations have been implicated in the maintenance of working memory representations. Notably, when memorised content is spatially lateralised, the power of posterior alpha activity exhibits corresponding lateralisation during the retention interval, consistent with the retinotopic organisation of the visual cortex. Beyond power, alpha frequency has also been linked to memory performan ce, with faster alpha rhythms associated with enhanced retention.
View Article and Find Full Text PDFLightweight hybrid Mamba2 for unsupervised medical image registration.
Med Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
View Article and Find Full Text PDF