AIMC Topic: Gene Expression Profiling

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Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.

International journal of medical sciences
The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from G...

Machine learning-driven identification of critical gene programs and key transcription factors in migraine.

The journal of headache and pain
BACKGROUND: Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associ...

Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble.

Scientific reports
Spaceflight has several detrimental effects on human and rodent health. For example, liver dysfunction is a common phenotype observed in space-flown rodents, and this dysfunction is partially reflected in transcriptomic changes. Studies linking trans...

Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene.

Journal of cardiothoracic surgery
BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have ...

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Science advances
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...

Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.

BMC medical informatics and decision making
BACKGROUND: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.  METHODS: In this study, di...

Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.

Frontiers in immunology
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving ...

Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer.

Frontiers in immunology
BACKGROUND: The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy...

Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms.

Scientific reports
Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecul...

Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through experiments.

Frontiers in immunology
INTRODUCTION: While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form with a median survival time of just five months. Conventional therapies offer limited benefits for ...