AIMC Topic: Gene Expression Profiling
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Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.
Renal failure
Dec 1, 2025
Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerulonephritis. Oxidative stress plays a critical role in the development and progression of ANCA-associ...
Transcriptome analysis and machine learning methods reveal potential mechanisms of zebrafish muscle aging.
Comparative biochemistry and physiology. Part D, Genomics & proteomics
Sep 1, 2025
Muscle is one of the most abundant tissues in the human body, and its aging usually leads to many adverse consequences. Zebrafish is a powerful model used to study human muscle diseases, yet we know little about the molecular mechanisms of muscle agi...
Screening and preliminary analysis of antimicrobial peptide genes in Octopussinensis.
Fish & shellfish immunology
Aug 1, 2025
Antimicrobial peptides (AMPs) are small molecular peptides that widely exist in organisms to resist external microbial invasion and play a crucial role in the host's immune defense system. Owing to their functions of efficient broad-spectrum killing ...
Pan-Cancer Spatial Profiling Reveals Conserved Subtypes and Niches of Cancer-Associated Fibroblasts.
Cancer research
Jul 15, 2025
Solid cancers are complex "ecosystems" comprised of diverse cell types and extracellular molecules, in which heterotypic interactions significantly influence disease etiology and therapeutic response. However, our current understanding of tumor micro...
Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.
Briefings in bioinformatics
Jul 2, 2025
Single-cell sequencing technology has profoundly revolutionized the field of cancer genomics, enabling researchers to explore gene expression profiles at the resolution of individual cells. Despite its extensive applications in the study of cancer ge...
Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.
Cancer research
Jul 2, 2025
UNLABELLED: Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving ch...
Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic Modelling.
Journal of cellular and molecular medicine
Jul 1, 2025
BACKGROUND: The global increase in thyroid cancer prevalence, particularly among female populations, underscores critical gaps in our understanding of molecular pathogenesis and diagnostic capabilities. Our investigation addresses these knowledge def...
Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.
Toxicological sciences : an official journal of the Society of Toxicology
Jul 1, 2025
Transcriptomic analyses have been an effective approach to investigate the biological responses and metabolic perturbations by environmental contaminants in rodent models. However, it is well recognized that metabolic networks are highly connected an...
Identification and analysis of diagnostic markers related to lactate metabolism in myocardial infarction.
Pathology, research and practice
Jul 1, 2025
Lactate metabolism is implicated in myocardial infarction (MI), yet the underlying mechanisms are not fully understood. Identifying lactate metabolism-related genes (LMRGs) could uncover new diagnostic and therapeutic targets for MI. We conducted a b...
Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.
Clinical and experimental pharmacology & physiology
Jul 1, 2025
Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artific...
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