AIMC Topic: Gene Expression Profiling

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Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. Th...

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma.

Frontiers in immunology
BACKGROUND: Increasing evidence indicates a connection between intracranial aneurysm (intracranial aneurysm, IA) and autoimmune diseases. However, the molecular mechanisms from a genetic perspective remain unclear. This study aims to elucidate the po...

The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.

Frontiers in immunology
OBJECTIVE: Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. This study aims to investigate the relationship between gene e...

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

Frontiers in immunology
BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. T...

Identification and Validation of Ferritinophagy-Related Biomarkers in Periodontitis.

International dental journal
OBJECTIVE: While ferritinophagy is believed to play a significant role in the development of periodontitis, the exact mechanisms remain unclear. This study aimed to investigate the biomarkers associated with ferritinophagy in periodontitis using tran...

Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.

Frontiers in immunology
INTRODUCTION: Aging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).

Revealing the Oxidative Stress-Related Molecular Characteristics and Potential Therapeutic Targets of Schizophrenia through Integrated Gene Expression Data Analysis.

Molecular neurobiology
Schizophrenia is a severe mental disorder characterized by oxidative stress imbalances. The underlying mechanisms of oxidative stress-related gene expression in schizophrenia require further investigation. Additionally, the diagnosis of schizophrenia...

A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas.

International journal of cancer
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with unpredictable recurrence. A be...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Scientific reports
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...

The role of machine learning in predicting titanium dioxide nanoparticles induced pulmonary pathology using transcriptomic biomarkers.

Journal of hazardous materials
This study explores the application of machine learning (ML) in identifying transcriptomic changes associated with pulmonary pathologies induced by titanium dioxide nanoparticles (TiO-NPs). Such an approach significantly contributes to understanding ...