AIMC Topic: Gene Expression Profiling

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Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.

Frontiers in endocrinology Mar 27, 2025
PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in immune cell infiltration.
Databases, Genetic Gene Expression Profiling Humans Mitochondria Diabetes Mellitus, Type 2 Gene Regulatory Networks Machine Learning
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

Scientific reports Mar 26, 2025
This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed g...
Computational Biology Shock, Septic Matrix Metalloproteinase 8 Child ROC Curve Humans Machine Learning Biomarkers Female Male Gene Expression Profiling
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Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.

Journal of medical systems Mar 26, 2025
Breast cancer classification using gene expression data presents significant challenges due to high dimensionality and complexity. This study introduces a novel hybrid framework integrating the Kashmiri Apple Optimization Algorithm (KAO) and the Arma...
Humans Algorithms Female Breast Neoplasms Gene Expression Profiling Support Vector Machine
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Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke.

Frontiers in immunology Mar 26, 2025
BACKGROUND: Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes associated with IS and explore their roles in disease mecha...
Mice Computational Biology Humans Multiomics Machine Learning Gene Expression Profiling Ischemic Stroke Transcriptome Animals Nucleotides Molecular Docking Simulation Gene Regulatory Networks
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning.

PloS one Mar 25, 2025
BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society.
Humans Machine Learning Glucocorticoids Computational Biology Transcriptome Molecular Docking Simulation Cluster Analysis Case-Control Studies Gene Expression Profiling Gene Regulatory Networks Lupus Erythematosus, Systemic
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Bioinformatics and machine learning approaches to explore key biomarkers in muscle aging linked to adipogenesis.

BMC musculoskeletal disorders Mar 22, 2025
Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expre...
Biomarkers Aged Animals Humans Machine Learning Adipogenesis Mice Computational Biology Quadriceps Muscle Male Aging Gene Expression Profiling
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Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.

European journal of medical research Mar 22, 2025
BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.
Machine Learning Biomarkers Female Humans Gene Regulatory Networks Inflammation Ischemic Stroke Male Gene Expression Profiling Transcriptome
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Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Computational biology and chemistry Mar 21, 2025
Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DT...
Gene Expression Profiling Transcriptome Time Factors Humans Artificial Intelligence Machine Learning MCF-7 Cells
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scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.

Genome biology Mar 21, 2025
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensio...
Single-Cell Analysis Humans Autoencoder Software Gene Expression Profiling Transcriptome Deep Learning
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning.

Frontiers in immunology Mar 21, 2025
BACKGROUND: Recent studies have suggested a potential association between gastric cancer (GC) and myocardial infarction (MI), with shared pathogenic factors. This study aimed to identify these common factors and potential pharmacologic targets.
Myocardial Infarction Genetic Predisposition to Disease Nomograms Mendelian Randomization Analysis Transcriptome Genome-Wide Association Study Humans Machine Learning Stomach Neoplasms Gene Expression Profiling
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