- OmicsML/awesome|deep|learning|single|cell|papers🔍
- An introduction to representation learning for single|cell data analysis🔍
- Application of Deep Learning on Single|cell RNA Sequencing Data ...🔍
- Integrating Deep Supervised🔍
- CoupledClustering – integrative analysis of single|cell genomics ...🔍
- A self|supervised augmentation|free spatial clustering method ...🔍
- A contrastive learning approach to integrate spatial transcriptomics ...🔍
- Machine learning integrative approaches to advance computational ...🔍
Self|supervised contrastive learning for integrative single cell RNA ...
OmicsML/awesome-deep-learning-single-cell-papers - GitHub
... Single-Cell RNA Sequencing Data via Deep Contrastive Learning [paper]. Single ... self-training for single-cell RNA sequence data [paper]; [2020 Nature ...
An introduction to representation learning for single-cell data analysis
... contrastive learning models, and semi-supervised models (Table 1). As an example, Park et al. demonstrated contrastive learning using ivis on scRNA-seq of ...
Application of Deep Learning on Single-cell RNA Sequencing Data ...
... cell for self-supervised contrastive learning, 2021, [82]. resVAE, VAE, https ... Using contrastive learning loss, i.e., NCE for the integration of multiple ...
Integrating Deep Supervised, Self-Supervised and Unsupervised ...
Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation. by. Liang Chen. Liang ...
CoupledClustering – integrative analysis of single-cell genomics ...
(A) Single-cell gene expression and single-cell chromatin accessibility data. (B) Learning coupling matrix from public data. (C) Coupled clustering model. (D) ...
A self-supervised augmentation-free spatial clustering method ...
A parameter-free deep embedded clustering method for single-cell RNA-seq data ... Integrated analysis of multimodal single-cell data. Cell ...
Application of Deep Learning on Single-cell RNA Sequencing Data ...
of the same cell for self-supervised contrastive learning. 2021. [82]. resVAE ... Using contrastive learning loss, i.e., NCE for the integration of.
A contrastive learning approach to integrate spatial transcriptomics ...
Spatially resolved, highly multiplexed RNA profiling in single cells ... 26. Hao, Y. ∙ Hao, S. ∙ Andersen-Nissen, E. ... Integrated analysis of ...
Software Web: A re-trainable deep learning tool for single cell RNA-sequencing based cell type labeling ... Description: A bimodal supervised contrastive learning ...
scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data ...
Therefore, using graph contrastive learning for cell clustering is a ... Deep soft K-means clustering with self-training for single-cell RNA sequence data.
Machine learning integrative approaches to advance computational ...
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA ...
Integrative analysis of single-cell multiomics data using deep learning
Single-cell RNA sequencing (scRNA-seq) has offered a ... nfeatures_pro = nfeatures_pro hidden_dim = hidden_rna + hidden_pro self.
GRANITE – an integrative genomic tool for complex data analysis
MGI Tech Partners with Human Cell Atlas to expand single cell RNA sequencing and spatial transcriptomics access; rna-seq MST-m6A – a novel multi ...
Integrative analysis of single-cell genomics data by coupled ... - PNAS
The method is illustrated by the integrative analysis of single-cell RNA ... The bulk datasets used in our simulation study are from two very similar cell ...
FB5P-seq: FACS-Based 5-Prime End Single-Cell RNA ... - Frontiers
We believe that our novel integrative scRNA-seq method will be a valuable option to study rare adaptive immune cell subsets in immunology research. Introduction.
Single‐cell RNA sequencing technologies and applications: A brief ...
... single-cell level for over millions of cells in a single study. ... compared five established supervised machine learning methods as ...
Deep Learning in Single-cell Analysis - ACM Digital Library
Deep soft K-means clustering with self-training for single-cell RNA sequence data, Python. scVI [192], AE, Deep generative modeling for single-cell ...
Comprehensive Integration of Single-Cell Data
A discriminative learning approach to differential expression analysis for single-cell RNA-seq ... 62. Raina, R. ∙ Battle, A. ∙ Lee, H. ... Self-taught Learning: ...
Integrative analyses of bulk and single-cell RNA-seq reveals the ...
In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression ...
Bulk and single-cell RNA-sequencing analyses along with abundant ...
We integrated up to 10 machine learning algorithms including random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, ...