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Single-Cell Gene Expression Analysis

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Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Frontiers in endocrinology
BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and A...

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

International journal of molecular sciences
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations...

scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.

BMC genomics
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose...

Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

Journal of biomedical informatics
OBJECTIVE: Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-...

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Briefings in bioinformatics
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analys...

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

International journal of molecular sciences
A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pat...

A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

STAR protocols
Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capt...

DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.

Scientific reports
Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data,...

Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma.

Genes and immunity
Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to av...