AIMC Topic: Single-Cell Analysis

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Conditional similarity triplets enable covariate-informed representations of single-cell data.

BMC bioinformatics Feb 9, 2025
BACKGROUND: Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per individual cell. In order to translate immune signatures assayed from blood or tissue into powe...
Single-Cell Analysis Computational Biology Humans Machine Learning Algorithms
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Integration of single-cell and bulk RNA sequencing data using machine learning identifies oxidative stress-related genes LUM and PCOLCE2 as potential biomarkers for heart failure.

International journal of biological macromolecules Feb 8, 2025
Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, ...
Mice Sequence Analysis, RNA Disease Models, Animal Male Heart Failure Machine Learning Humans Extracellular Matrix Proteins Oxidative Stress Biomarkers Lumican Single-Cell Analysis Animals
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Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Nature communications Feb 8, 2025
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...
Humans Disease Progression Cell Line, Tumor Single-Cell Analysis Machine Learning Animals Neoplasms Genomic Instability Mice DNA Replication DNA Copy Number Variations DNA Replication Timing Genetic Heterogeneity
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A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Nature communications Feb 3, 2025
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory ...
Humans Gene Expression Regulation Neural Networks, Computer Hair Follicle Animals Mice Diabetes Mellitus, Type 2 Polymorphism, Single Nucleotide Single-Cell Analysis Transcription Factors Gene Regulatory Networks
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Deep learning powered single-cell clustering framework with enhanced accuracy and stability.

Scientific reports Feb 3, 2025
Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this exploration, allows for the identification of distinct cell types within a population. Graph-based de...
Humans Single-Cell Analysis Algorithms RNA-Seq Gene Expression Profiling Sequence Analysis, RNA Cluster Analysis Deep Learning
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Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning.

Biology direct Jan 29, 2025
BACKGROUND: Carotid atherosclerotic plaque is the primary cause of cardiovascular and cerebrovascular diseases. It is closely related to oxidative stress and immune inflammation. This bioinformatic study was conducted to identify key oxidative stress...
Oxidative Stress Single-Cell Analysis Plaque, Atherosclerotic Humans Machine Learning Carotid Artery Diseases Macrophages Transcriptome
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scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.

BMC bioinformatics Jan 29, 2025
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology...
Computational Biology Sequence Analysis, RNA Cluster Analysis Deep Learning Humans Single-Cell Analysis Gene Expression Profiling RNA-Seq
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Multimodal learning for mapping genotype-phenotype dynamics.

Nature computational science Jan 28, 2025
How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers...
Gene Expression Profiling Transcriptome Single-Cell Analysis Humans Genotype von Willebrand Factor Machine Learning Genetic Association Studies Phenotype Computational Biology
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Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.

Biochimica et biophysica acta. Molecular basis of disease Jan 25, 2025
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (sc...
Sequence Analysis, RNA Colorectal Neoplasms Pyridones Antineoplastic Agents Humans Pyrimidinones Drug Resistance, Neoplasm Organoids Single-Cell Analysis Machine Learning Adenomatous Polyposis Coli
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Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

Frontiers in immunology Jan 24, 2025
INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs sing...
Ion Transport RNA-Seq Gene Regulatory Networks Metals Single-Cell Gene Expression Analysis Diabetes Mellitus, Type 2 Humans Single-Cell Analysis Machine Learning Protein Interaction Maps Gene Expression Profiling
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