AIMC Topic: Transcriptome

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

Bioinformatics (Oxford, England) Mar 29, 2025
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...
Sequence Analysis, RNA Gene Expression Profiling Transcriptome Single-Cell Analysis Humans Machine Learning Algorithms
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A multi-view graph convolutional network framework based on adaptive adjacency matrix and multi-strategy fusion mechanism for identifying spatial domains.

Bioinformatics (Oxford, England) Mar 29, 2025
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....
Neural Networks, Computer Software Single-Cell Analysis Algorithms Computational Biology Gene Expression Profiling Transcriptome
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uHAF: a unified hierarchical annotation framework for cell type standardization and harmonization.

Bioinformatics (Oxford, England) Mar 29, 2025
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...
Single-Cell Analysis Computational Biology Software Humans Molecular Sequence Annotation Gene Expression Profiling Machine Learning Transcriptome
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A deep learning tissue classifier based on differential co-expression genes predicts the pregnancy outcomes of cattle†.

Biology of reproduction Mar 16, 2025
Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artifi...
Animals Gene Expression Profiling Transcriptome Pregnancy Female Pregnancy Outcome Endometrium Deep Learning Insemination, Artificial Cattle Embryo Transfer
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Multi-Manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics.

Briefings in bioinformatics Mar 4, 2025
Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insights into the pathogenesis of gene expression. Increasingly, computational methods based on graph neural networks are being developed for spatial trans...
Gene Expression Profiling Transcriptome Humans Computational Biology Neural Networks, Computer Algorithms
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Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.

Briefings in bioinformatics Mar 4, 2025
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...
Humans Gene Expression Profiling Transcriptome Deep Learning Data Science Single-Cell Analysis Computational Biology
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Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.

Briefings in bioinformatics Mar 4, 2025
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocat...
Supervised Machine Learning Learning Curve Gene Expression Profiling Transcriptome Computational Biology Sequence Analysis, RNA MicroRNAs Sample Size Humans Machine Learning Algorithms
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MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.

Briefings in bioinformatics Mar 4, 2025
Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detectin...
Neural Networks, Computer Software Algorithms Humans Transcriptome Mice Computational Biology Brain Animals Gene Expression Profiling Hippocampus Autoencoder
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COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics (Oxford, England) Mar 4, 2025
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive d...
High-Throughput Nucleotide Sequencing Gene Expression Profiling Machine Learning Transcriptome RNA-Seq Single-Cell Analysis Software Humans Computational Biology Algorithms Sequence Analysis, RNA
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Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.

Journal of cellular and molecular medicine Mar 1, 2025
Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism-related genes. Through integrated analysis of TCGA and GEO datasets, we established a...
Glycolipids Esophageal Squamous Cell Carcinoma Gene Expression Profiling Transcriptome Cell Line, Tumor Mice Prognosis Gene Expression Regulation, Neoplastic Humans Biomarkers, Tumor Esophageal Neoplasms Machine Learning Animals
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