AIMC Topic: Gene Expression Profiling

Clear Filters Showing 101 to 110 of 1601 articles

Spatial domain identification method based on multi-view graph convolutional network and contrastive learning.

PLoS computational biology
Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structu...

Machine learning identifies MiRNA biomarkers and immune mechanisms in active tuberculosis.

Scientific reports
Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global public health threat. The rising prevalence of HIV/TB co-infection and multidrug-resistant tuberculosis (MDR-TB) has further intensified this challenge. This study ...

Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug response.

Journal of experimental & clinical cancer research : CR
The high intra-patient heterogeneity in multifocal primary prostate cancer (PCa) has curtailed the efficacy of current treatment options. By employing twin biopsies from multiple lesions with matched patient-derived organoids (PDO) models, the PCa mo...

Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation.

Renal failure
Diabetic kidney disease (DKD) remains a critical challenge in diabetes management, necessitating a deep understanding of its molecular underpinnings for better diagnosis and treatment strategies. This study was conducted to identify and validate nove...

Identification and validation of aryl hydrocarbon receptor-associated hub genes in ulcerative colitis via integrated bioinformatics analysis.

Human genomics
OBJECTIVE: Ulcerative colitis (UC), a chronic inflammatory bowel disease, continues to pose substantial challenges in both diagnosis and treatment. The aryl hydrocarbon receptor (AhR) plays a pivotal role in intestinal immune regulation; however, its...

Identification of biomarkers related to neutrophil extracellular traps and potential therapeutic drugs for rheumatoid arthritis using computational analysis.

European journal of medical research
BACKGROUND: Neutrophil extracellular traps (NETs) derived from neutrophils are implicated in the pathogenesis of rheumatoid arthritis (RA) pathogenicity, though the underlying mechanisms remain unclear.

Gene association study between polycystic ovary syndrome and metabolic syndrome: a transcriptomic analysis and machine learning approach.

Journal of ovarian research
BACKGROUND: Patients with polycystic ovary syndrome (PCOS) often experience a range of metabolic comorbidities, suggesting a potential association between PCOS and metabolic syndrome (MetS). However, this potential link has not yet been fully elucida...

Single-cell RNA-seq combined with bulk RNA-seq analysis identifies necroptosis-related genes as therapeutic targets for periodontitis.

BMC medical genomics
BACKGROUND: Necroptosis, a regulated form of programmed cell death, exacerbates inflammatory responses by releasing damage-associated molecular patterns and inflammatory factors. However, the specific mechanisms underlying necroptosis in periodontiti...

Bioinformatics identification of key genes correlating NOD1 and Endoplasmic Reticulum stress in Hepatitis B virus-induced acute liver failure.

Scientific reports
Endoplasmic reticulum stress (ERS) has been implicated in a range of biological processes, yet its specific involvement in Hepatitis B virus-associated acute liver failure (HBV-ALF) remains poorly understood. This study aimed to identify key ERS-rela...

A prognostic model for gastric cancer constructed by multiple machine learning algorithms.

Journal of molecular histology
Gastric cancer (GC) is a highly heterogeneous disease that requires highly accurate prognostic models. Machine learning is a powerful tool for identifying predictive biomarkers and developing prognostic models. Here, we aim to integrate bioinformatic...