AIMC Topic: Single-Cell Analysis

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Oxidative Phosphorylation Pathway in Ankylosing Spondylitis: Multi-Omics Analysis and Machine Learning.

International journal of rheumatic diseases
INTRODUCTION: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the axial skeleton, characterized by immune microenvironment dysregulation and elevated cytokines like TNF-α and IL-17. Mitochondrial oxidative phosphorylation (OXP...

Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

Medicine
BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.

Towards a unified framework for single-cell -omics-based disease prediction through AI.

Clinical and translational medicine
Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now of...

adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.

Bioinformatics (Oxford, England)
MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (scRNA-seq) data have been proposed for biomedical purposes. This raises concerns about their vulnerabi...

A multi-view graph convolutional network framework based on adaptive adjacency matrix and multi-strategy fusion mechanism for identifying spatial domains.

Bioinformatics (Oxford, England)
MOTIVATION: Spatial transcriptomics (ST) addresses the loss of spatial context in single-cell RNA-sequencing by simultaneously capturing gene expression and spatial location information. A critical task of ST is the identification of spatial domains....

uHAF: a unified hierarchical annotation framework for cell type standardization and harmonization.

Bioinformatics (Oxford, England)
SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, w...

FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.

Briefings in bioinformatics
Single-cell multi-omics technologies have revolutionized the study of cell states and functions by simultaneously profiling multiple molecular layers within individual cells. However, existing methods for integrating these data struggle to preserve c...

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.

Briefings in bioinformatics
The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and s...

CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.

Briefings in bioinformatics
With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding t...

Graph neural networks for single-cell omics data: a review of approaches and applications.

Briefings in bioinformatics
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...