AIMC Topic: Gene Expression Profiling

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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...
Gene Expression Profiling Transcriptome Computational Biology Sequence Analysis, RNA Humans Machine Learning Algorithms MicroRNAs Sample Size Supervised Machine Learning Learning Curve
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Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

Briefings in bioinformatics Mar 4, 2025
High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules,...
Humans Neural Networks, Computer Algorithms Prognosis Computational Biology Biomarkers, Tumor Gene Expression Regulation, Neoplastic Neoplasms Proportional Hazards Models Gene Expression Profiling Graph Neural Networks
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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...
Autoencoder Mice Computational Biology Hippocampus Humans Neural Networks, Computer Brain Software Animals Algorithms Gene Expression Profiling Transcriptome
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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...
Humans Machine Learning Software RNA-Seq Computational Biology Sequence Analysis, RNA High-Throughput Nucleotide Sequencing Algorithms Gene Expression Profiling Transcriptome Single-Cell Analysis
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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...
Transcriptome Cell Line, Tumor Mice Gene Expression Regulation, Neoplastic Prognosis Esophageal Neoplasms Biomarkers, Tumor Glycolipids Animals Esophageal Squamous Cell Carcinoma Gene Expression Profiling Humans Machine Learning
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Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.

Cancer medicine Mar 1, 2025
BACKGROUND: Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.
Middle Aged Gene Expression Profiling ROC Curve Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Kaplan-Meier Estimate Female Stomach Neoplasms Biomarkers, Tumor Male Machine Learning Algorithms Prognosis Humans
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stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.

Nucleic acids research Feb 27, 2025
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression leve...
Molecular Sequence Annotation Humans Deep Learning Software Single-Cell Analysis Animals RNA-Seq Gene Expression Profiling Sequence Analysis, RNA Transcriptome
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Constructing a Prognostic Model for Subtypes of Colorectal Cancer Based on Machine Learning and Immune Infiltration-Related Genes.

Journal of cellular and molecular medicine Feb 1, 2025
This study constructed a prognostic model combining machine learning-based immune infiltration-related genes in each CRC subtype. We used publicly accessible gene expression data and clinical information on colorectal cancer patients. Integrated bioi...
Biomarkers, Tumor Male Gene Expression Profiling ROC Curve Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Algorithms DNA Copy Number Variations Humans Prognosis Machine Learning Female Colorectal Neoplasms
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CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.

Cell genomics Jan 8, 2025
Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and hav...
Gene Expression Profiling Humans Mice Brain Single-Cell Analysis Sequence Analysis, RNA RNA-Seq Animals Unsupervised Machine Learning Single-Cell Gene Expression Analysis
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scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Nucleic acids research Jan 6, 2025
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-...
Databases, Genetic Knowledge Bases Quantitative Trait Loci Genome-Wide Association Study Single-Cell Analysis Humans Gene Regulatory Networks Software Gene Expression Profiling Transcriptome
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