AIMC Topic: Transcriptome

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scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis.

Genome biology Jun 24, 2024
Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method desig...
Single-Cell Analysis Mice Computational Biology Machine Learning Tumor Microenvironment Humans Animals Transcriptome Cell Communication
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Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Frontiers in immunology Jun 24, 2024
Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrop...
Transcriptome Female Intracranial Aneurysm Male Single-Cell Analysis Cellular Microenvironment Computational Biology Humans Gene Expression Profiling Gene Regulatory Networks Monocytes Machine Learning Macrophages
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NNICE: a deep quantile neural network algorithm for expression deconvolution.

Scientific reports Jun 18, 2024
The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition ...
Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Computational Biology Gene Expression Profiling Neural Networks, Computer Transcriptome Algorithms Deep Learning Humans
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Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

World journal of surgical oncology Jun 13, 2024
BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critica...
Single-Cell Analysis Lung Neoplasms Gene Expression Regulation, Neoplastic Disease Progression Gene Expression Profiling Transcriptome Carcinoma, Non-Small-Cell Lung Humans Machine Learning Biomarkers, Tumor Male Prognosis Female
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Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.

Nature communications Jun 11, 2024
Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerfu...
Neoplasms Transcriptome Deep Learning Lung Neoplasms Humans Phenotype Supervised Machine Learning
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Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.

Frontiers in immunology Jun 10, 2024
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF1...
Female Male Molecular Docking Simulation Alzheimer Disease Humans Gene Expression Profiling Receptors, TNF-Related Apoptosis-Inducing Ligand Machine Learning Transcriptome Biomarkers Gene Regulatory Networks
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Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment.

Immunobiology Jun 9, 2024
BACKGROUND: Osteoarthritis (OA) is a prevalent joint disorder characterized by cartilage degeneration and joint inflammation. Liquid-liquid phase separation (LLPS), a biophysical process involved in cellular organization, has recently gained attentio...
Chondrocytes Osteoarthritis Phase Separation Humans Algorithms Machine Learning Transcriptome Biomarkers Computational Biology Gene Expression Profiling Interleukin-1beta
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Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity.

Biomolecules Jun 9, 2024
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local...
Gene Expression Profiling Transcriptome Humans Algorithms Deep Learning Computational Biology
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Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing.

British journal of cancer Jun 7, 2024
BACKGROUND: It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for ident...
Receptors, Antigen, T-Cell Antigens, Neoplasm Lymphocytes, Tumor-Infiltrating CD8-Positive T-Lymphocytes Humans Tumor Microenvironment Gastrointestinal Neoplasms Machine Learning Transcriptome Single-Cell Analysis
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Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Human genomics Jun 5, 2024
BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with rec...
Gene Expression Profiling Transcriptome Deep Learning Liver Neoplasms Biomarkers, Tumor Humans Male Female Middle Aged Gene Expression Regulation, Neoplastic Signal Transduction Carcinoma, Hepatocellular Protein Interaction Maps Gene Regulatory Networks Neoplasm Recurrence, Local Liver Transplantation
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