AIMC Topic: Gene Expression Profiling

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Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures.

Translational psychiatry
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high...

Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure.

PloS one
Heart failure (HF) represents a significant public health concern, characterized by elevated rates of mortality and morbidity. Recent advancements in gene sequencing technologies have led to the identification of numerous genes associated with heart ...

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation.

Annals of medicine
BACKGROUND: Acute kidney injury (AKI) is a prevalent and life-threatening condition characterized by abrupt renal function decline and subsequent inflammatory cascades. PANoptosis has emerged as a significant contributor to the pathophysiology of AKI...

Machine learning-based transcriptomic analysis identifies candidate genes in sepsis-induced coagulopathy and explores the immunomodulatory potential of baicalein.

Human genomics
BACKGROUND: Sepsis is a major contributor to high morbidity and mortality, often leading to coagulation disorders (CD) in affected individuals. Baicalein, a natural compound with well-established anti-inflammatory properties, shows promise as a poten...

Identification and experimental validation of biomarkers related to mitochondrial and programmed cell death in obsessive-compulsive disorder.

Scientific reports
Background Mitochondrial-related genes (MRGs) and programmed cell death-related genes (PCD-RGs) have been proven to play important roles in obsessive-compulsive disorder (OCD), and identifying their shared biomarkers is conducive to the diagnosis and...

Integrative single-cell and bulk transcriptomic analysis reveals the landscape of T cell mitotic catastrophe associated genes in esophageal squamous cell carcinoma.

Human genomics
BACKGROUND: Mitotic catastrophe (MC) is a well-recognized endogenous mechanism of tumor cell death, characterized as a delayed cell death process associated with aberrant mitosis. However, its prognostic significance in the context of intratumoral he...

Adipose tissue gene expression and longitudinal clinical phenotypes are early biomarkers of lipid-regulating drug usage.

Scientific reports
Cardiovascular disease progression is characterised by the dysregulation of lipid metabolism and pro-atherogenic effects of adipose tissue signalling. Recent findings from the analysis of transcriptomic data in bulk tissue has enabled these insights ...

Utility of the continuous spectrum formed by pathological states in characterizing disease properties.

NPJ systems biology and applications
Understanding diseases as the result of continuous transitions from a healthy system is more realistic than understanding them as discrete states. Here, we designed the spectrum formation approach (SFA), a machine learning-based method that extracts ...

Integrative analysis of transcriptome and metabolome profiles reveals immune-metabolic alterations in pulmonary sarcoidosis.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Pulmonary sarcoidosis, a disease of unknown etiology, is characterized by the presence of noncaseating granulomas in lung parenchyma. This present study combines metabolomic and transcriptomic data to determine the metabolic and different...

Integrated single-cell and clinical transcriptomic analysis identifies blunted glycolytic activation as a hallmark of maladaptive repair in renal ischemia-reperfusion.

Renal failure
Acute kidney injury (AKI) is a common and increases risk of chronic kidney disease (CKD). While mitochondrial dysfunction drives maladaptive repair, the role of glycolysis in renal recovery remains unclear. Here, we integrated single-cell transcripto...