The impacts of active and self-supervised learning on efficient annotation of single-cell expression data
MJ Geuenich, D Gong, KR Campbell - Nature Communications, 2024 - nature.com
A crucial step in the analysis of single-cell data is annotating cells to cell types and states.
While a myriad of approaches has been proposed, manual labeling of cells to create training …
While a myriad of approaches has been proposed, manual labeling of cells to create training …
Methods for cell-type annotation on scRNA-seq data: A recent overview
The evolution of single-cell technology is ongoing, continually generating massive amounts
of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks …
of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks …
Robust graph regularized NMF with dissimilarity and similarity constraints for ScRNA-seq data clustering
Z Shu, Q Long, L Zhang, Z Yu… - Journal of Chemical …, 2022 - ACS Publications
The notable progress in single-cell RNA sequencing (ScRNA-seq) technology is beneficial
to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely …
to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely …
Automatic cell type annotation using marker genes for single-cell RNA sequencing data
Y Chen, S Zhang - Biomolecules, 2022 - mdpi.com
Recent advancement in single-cell RNA sequencing (scRNA-seq) technology is gaining
more and more attention. Cell type annotation plays an essential role in scRNA-seq data …
more and more attention. Cell type annotation plays an essential role in scRNA-seq data …
NeuroDAVIS: A neural network model for data visualization
The task of dimensionality reduction and visualization of high-dimensional datasets remains
a challenging problem since long. Modern high-throughput technologies produce large …
a challenging problem since long. Modern high-throughput technologies produce large …
scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision
J Yang, W Wang, X Zhang - Bioinformatics, 2024 - academic.oup.com
Motivation Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell
data analysis. Although a host of works have been developed, the low signal-to-noise-ratio …
data analysis. Although a host of works have been developed, the low signal-to-noise-ratio …
A Semi-Supervised Learning-Based Method for Recognizing Volleyball Players' Arm Movement Trajectories
S Ming - International Journal of High Speed Electronics and …, 2024 - World Scientific
In order to realize the recognition of athletes' arm trajectories with low data labeling cost, a
semi-supervised learning-based method is proposed for volleyball players' arm trajectory …
semi-supervised learning-based method is proposed for volleyball players' arm trajectory …
scMinerva: an Unsupervised Graph Learning Framework with Label-efficient Fine-tuning for Single-cell Multi-omics Integrated Analysis
Single-cell multi-omics is a rapidly growing field in biomedicine, where multiple biological
contents, such as the epigenome, genome, and transcriptome, can be measured …
contents, such as the epigenome, genome, and transcriptome, can be measured …
[PDF][PDF] scMinerva: an Unsupervised Graph Learning Framework with Label-efficient Fine-tuning for Single-cell Multi-omics Integrated Analysis
YU Tingyang, Y Zong, Y Wang, X Wang, Y Li - biorxiv.org
Single-cell multi-omics is a rapidly growing field in biomedicine, where multiple biological
contents, such as the epigenome, genome, and transcriptome, can be measured …
contents, such as the epigenome, genome, and transcriptome, can be measured …