Single-Cell Analysis

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

Frontiers in immunology 40330458

OBJECTIVE: This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to con...

Biomarkers, Tumor Computational Biology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Male Multiomics Precision Medicine Prognosis Single-Cell Analysis Tumor Microenvironment Urinary Bladder Neoplasms

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Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology 40308601

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...

Artificial Intelligence Biomarkers, Tumor Breast Neoplasms Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Prognosis RNA-Binding Proteins Single-Cell Analysis Transcriptome Tumor Microenvironment

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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 40276515

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...

Biomarkers Fatty Acids Gene Expression Profiling Humans Machine Learning Monocytes Mucocutaneous Lymph Node Syndrome Sequence Analysis, RNA Single-Cell Analysis Transcriptome

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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 40332226

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...

Biomarkers, Tumor Breast Neoplasms Cell Death Female Gene Expression Regulation, Neoplastic Humans Machine Learning Middle Aged Neoadjuvant Therapy Prognosis Sequence Analysis, RNA Single-Cell Analysis

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Effect of Cell-Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System.

Analytical chemistry 40298933

Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail to disclose the heterogeneity at a single-cell resolution. Single-cell coculture systems that isolat...

Cell Communication Cell Line, Tumor Cell Movement Cell Proliferation Coculture Techniques Deep Learning High-Throughput Screening Assays Human Umbilical Vein Endothelial Cells Humans Single-Cell Analysis

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Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

Scientific reports 40275058

Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right heart failure and death. Despite advancements in diagnosis and treatment, it remains incurable, and ...

Adult Female Gene Expression Profiling Humans Hypoxia Machine Learning Male Middle Aged Pulmonary Arterial Hypertension Single-Cell Analysis Transcriptome

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Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports 40275021

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Biomarkers, Tumor Brain Neoplasms Deep Learning DNA Copy Number Variations Female Glioblastoma Glioma Humans Male Multiomics Mutation Prognosis Single-Cell Analysis Transcriptome Tumor Microenvironment

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Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data.

Briefings in bioinformatics 40315434

Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noi...

Carcinoma, Renal Cell Deep Learning Gene Expression Profiling Humans Neoplasms Phenotype Sequence Analysis, RNA Single-Cell Analysis Transcriptome

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

International journal of rheumatic diseases 40296690

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...

Adult Case-Control Studies Female Gene Expression Profiling Gene Regulatory Networks Humans Leukocytes, Mononuclear Machine Learning Male Middle Aged Mitochondria Multiomics Oxidative Phosphorylation RNA-Seq Signal Transduction Single-Cell Analysis Spondylitis, Ankylosing Transcriptome

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Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.

Clinical and experimental medicine 40382513

Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms we...

Biomarkers, Tumor Female Gene Expression Regulation, Neoplastic Humans Machine Learning Prognosis Receptors, Immunologic Single-Cell Analysis Triple Negative Breast Neoplasms Tumor Microenvironment

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Explore the latest research on artificial intelligence and machine learning in medicine.

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.

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.

Effect of Cell-Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System.

Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data.

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

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.

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