scGPT: toward building a foundation model for single-cell multi-omics using generative AI
Generative pretrained models have achieved remarkable success in various domains such
as language and computer vision. Specifically, the combination of large-scale diverse …
as language and computer vision. Specifically, the combination of large-scale diverse …
[HTML][HTML] Deep learning applications in single-cell genomics and transcriptomics data analysis
Traditional bulk sequencing methods are limited to measuring the average signal in a group
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
Computational methods for single-cell multi-omics integration and alignment
S Stanojevic, Y Li, A Ristivojevic… - Genomics, Proteomics …, 2022 - academic.oup.com
Recently developed technologies to generate single-cell genomic data have made a
revolutionary impact in the field of biology. Multi-omics assays offer even greater …
revolutionary impact in the field of biology. Multi-omics assays offer even greater …
Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
Deciphering the intricate relationships between transcription factors (TFs), enhancers, and
genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is …
genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is …
Single-cell omics: experimental workflow, data analyses and applications
Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
scFormer: a universal representation learning approach for single-cell data using transformers
Single-cell sequencing has emerged as a promising technique to decode cellular
heterogeneity and analyze gene functions. With the high throughput of modern techniques …
heterogeneity and analyze gene functions. With the high throughput of modern techniques …
scGAT: a cell-type annotation framework for single-cell transcriptomics using graph attention network and meta learning
J Dong, M Li, F Wang - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for
classifying cell types across various species, tissues, and environmental conditions, thereby …
classifying cell types across various species, tissues, and environmental conditions, thereby …
Single-cell classification, analysis, and its application using deep learning techniques
R Premkumar, A Srinivasan, KGH Devi, M Deepika… - Biosystems, 2024 - Elsevier
Single-cell analysis (SCA) improves the detection of cancer, the immune system, and
chronic diseases from complicated biological processes. SCA techniques generate high …
chronic diseases from complicated biological processes. SCA techniques generate high …
[HTML][HTML] Deep learning analysis of single‐cell data in empowering clinical implementation
Recent advances in single-cell sequencing technologies enable the characterization of
cellular heterogeneity and biological processes in complex diseases. This provides …
cellular heterogeneity and biological processes in complex diseases. This provides …
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 …