AIMC Topic: Transcriptome

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A Lactylation-Based Diagnostic Model Reveals Molecular Subtypes and Therapeutic Targets in Dilated Cardiomyopathy.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Dilated cardiomyopathy (DCM) is a progressive myocardial disorder lacking reliable molecular biomarkers for early diagnosis. Given the emerging role of protein lactylation in cardiovascular disease, we investigated lactylation-related genes (LRGs) in...

Machine learning reveal shared diagnostic biomarkers and convergent pathways in age-related hearing loss and sarcopenia.

Medicine
Age-related hearing loss (HL) and sarcopenia (ARS) are prevalent geriatric syndromes sharing common risk factors. This study aimed to identify shared biomarkers and elucidate convergent pathogenic mechanisms. Transcriptomic datasets were obtained fro...

Coincidence of the threshold temperature of seasonal switching for diel transcriptomic oscillations and growth.

Plant & cell physiology
Predicting plant responses to global warming is essential for ecosystem management and crop yields. As many genes are controlled by the circadian clock, understanding the effects of temperature on transcriptomic rhythmicity under natural conditions i...

Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.

Brain : a journal of neurology
Neurological disorders result from the complex and poorly understood contributions of many cell types. It is therefore essential to uncover mechanisms behind these disorders and identify specific therapeutic targets. Single-nucleus technologies have ...

Transcriptomic and single-cell insights into mitochondrial genes NDUFA8, ECI2, and ACADM in acute myocardial infarction.

Gene
Mitochondrial function plays a crucial role in understanding the pathogenesis of acute myocardial infarction.This study investigates mitochondrial function-related genes (MFRGs) in acute myocardial infarction (AMI) through bioinformatics and rigorous...

HL-BscPF: Hybrid learning facilitates brain cell auto-identification in multiple pathologies.

Life sciences
AIMS: The rapidly growing scale and complexity of single-cell transcriptomic data in brain research make it increasingly difficult for traditional methods to extract meaningful insights efficiently, highlighting the need for artificial intelligence.

Dissecting cross-lineage tumourigenesis under p53 inactivation through single-cell multi-omics and spatial transcriptomics.

Clinical and translational medicine
BACKGROUND: Tumour suppressor genes, exemplified by TP53 (encoding the human p53), function as critical guardians against tumourigenesis. Germline TP53-inactivating mutations underlie Li-Fraumeni syndrome, a hereditary cancer predisposition disorder ...

GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling.

Cancer research communications
UNLABELLED: A subset of cancers present with unclear or potentially incorrect primary histopathologic diagnoses, including cancers of unknown primary (CUP). We aimed to develop and validate an artificial intelligence (AI) tool, Genomic Probability Sc...

Genetic and molecular underpinnings of the link between rheumatoid arthritis and myasthenia gravis: Insights from GWAS and transcriptomic analyses.

Clinical rheumatology
BACKGROUND: Although studies have shown that patients with rheumatoid arthritis (RA) are at a higher risk of developing myasthenia gravis (MG), the causal relationship and shared genetic basis between these two diseases have not been fully investigat...

Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease and Heart Failure.

Computers in biology and medicine
AIM: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical challenges due to the poorly understood pathophysiological mechanisms driving their coexistence. In this ...