AIMC Topic: Single-Cell Analysis

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scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.

IET systems biology Apr 22, 2025
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often fa...
Supervised Machine Learning Neural Networks, Computer Deep Learning Single-Cell Analysis Computational Biology Humans
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Transfer learning of multicellular organization via single-cell and spatial transcriptomics.

PLoS computational biology Apr 21, 2025
Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) m...
Humans Computational Biology Gene Expression Profiling Sequence Analysis, RNA Transcriptome Neural Networks, Computer Drosophila Single-Cell Analysis Machine Learning Mice Animals
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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

BMC bioinformatics Apr 18, 2025
BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug d...
Single-Cell Gene Expression Analysis Computational Biology Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Gene Regulatory Networks Humans Machine Learning Algorithms Deep Learning
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Circular RNA discovery with emerging sequencing and deep learning technologies.

Nature genetics Apr 17, 2025
Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disea...
Artificial Intelligence Algorithms Animals Deep Learning Humans RNA, Circular Single-Cell Analysis Sequence Analysis, RNA High-Throughput Nucleotide Sequencing
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Molecular features and diagnostic modeling of synovium- and IPFP-derived OA macrophages in the inflammatory microenvironment via scRNA-seq and machine learning.

Journal of orthopaedic surgery and research Apr 17, 2025
BACKGROUND: Osteoarthritis (OA) is the leading cause of degenerative joint disease, with total joint replacement as the only definitive cure. However, no disease-modifying therapy is currently available. Inflammation and fibrosis in the infrapatellar...
Inflammation Synovial Membrane Rats, Sprague-Dawley Adipose Tissue RNA-Seq Single-Cell Gene Expression Analysis Rats Osteoarthritis Cellular Microenvironment Machine Learning Macrophages Female Animals Male Middle Aged Single-Cell Analysis Humans
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Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.

Nature Apr 16, 2025
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood. We use spatial proteomics by mass spectrometry and machine learning to map AATD in h...
alpha 1-Antitrypsin Deficiency alpha 1-Antitrypsin Liver Hepatocytes Unfolded Protein Response Female Male Disease Progression Humans Machine Learning Single-Cell Analysis Phenotype Proteomics Proteome Liver Cirrhosis
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Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology Apr 16, 2025
BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. T...
Biomarkers, Tumor Gene Expression Profiling Transcriptome Prognosis Humans Single-Cell Analysis Female Artificial Intelligence RNA-Binding Proteins Breast Neoplasms Machine Learning Gene Expression Regulation, Neoplastic Tumor Microenvironment
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Single-Cell Sequencing-Guided Annotation of Rare Tumor Cells for Deep Learning-Based Cytopathologic Diagnosis of Early Lung Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Apr 15, 2025
Deep learning (DL) models for medical image analysis are majorly bottlenecked by the lack of well-annotated datasets. Bronchoalveolar lavage (BAL) is a minimally invasive procedure to diagnose lung cancer, but BAL cytology suffers from low sensitivit...
Humans Female Lung Neoplasms Early Detection of Cancer Cytodiagnosis Bronchoalveolar Lavage Fluid Male Deep Learning Single-Cell Analysis
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Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

International journal of molecular sciences Apr 13, 2025
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Middle Aged Single-Cell Analysis Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Neoadjuvant Therapy Cell Death Prognosis Female Breast Neoplasms Biomarkers, Tumor Humans Machine Learning
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.

Frontiers in immunology Apr 10, 2025
BACKGROUND: The bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper u...
Transcriptome Machine Learning Mucocutaneous Lymph Node Syndrome Humans Single-Cell Analysis Fatty Acids Biomarkers Monocytes Gene Expression Profiling Sequence Analysis, RNA
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