AIMC Topic: Transcriptome

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Mechanistic exploration of hexokinase 2 and metabolism in diabetic cardiomyopathy.

Molecular medicine reports
The pathogenesis of diabetic cardiomyopathy (DCM) remains incompletely understood. The present study employed weighted gene co‑expression network analysis to analyze the DCM transcriptome dataset from the Gene Expression Omnibus (GEO) database to ide...

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Computational biology and chemistry
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...

Multi-omics identifies OSM-OSMR as a key receptor-ligand in the tumor environment of endometrial adenocarcinoma.

International immunopharmacology
Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advanced stages remain uncertain. Cell-cell communication plays a crucial role in the tumor microenvironmen...

Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.

Cancer research
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
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
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...

Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Clinical and experimental pharmacology & physiology
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...

Mesenchymal stem cell transplantation ameliorates inflammation in spinal cord injury by inhibiting lactylation-related genes.

Cytokine
BACKGROUND: The immune microenvironment significantly influences neural regeneration in spinal cord injury (SCI). Lactate activates central nervous system (CNS) glial cells, prompting the secretion of proinflammatory cytokines and triggering an infla...

Deciphering the Regulatory Networks of the Migrasome-Associated Cell Subpopulation in Heterotopic Ossification via Multi-Omics Analysis.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Heterotopic ossification (HO) is a pathological process where bone forms in extraskeletal tissues, often occurring as a complication of tissue repair following injury. This condition can lead to movement limitations, pain, and functional impairment. ...

Neuroimaging pattern interactions for suicide risk in depression captured by ensemble learning over transcriptome-defined parcellation.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined...