AIMC Topic: Gene Expression Profiling

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Cell death-related gene signatures as dual-function biomarkers: Early diagnosis and therapeutic targeting in Staphylococcus aureus pneumonia.

PloS one
BACKGROUND: Staphylococcus aureus (S. aureus) pneumonia constitutes a lethal respiratory infection with persistently high clinical mortality. Although programmed cell death (PCD) pathways are implicated in diverse disease processes, their mechanistic...

Elucidate senescence-related gene signature and immune infiltration landscape in abdominal aortic aneurysm.

PloS one
BACKGROUND: Abdominal aortic aneurysm (AAA) refers to a lasting enlargement of the abdominal aorta. Senescence, a major risk factor of AAA, demonstrate positive connection with both the formation and rupture of aneurysms. Therefore, investigating the...

Decoding Non-Neuronal Mechanisms and Therapeutic Targets in Huntington's Disease Through Integrative Transcriptomics and Machine Learning.

Journal of molecular neuroscience : MN
Huntington's disease (HD) is a rare, inherited neurodegenerative disorder caused by the expanded CAG repeats in the huntingtin gene. The HD domain still lacks detailed knowledge of validated drug targets, limiting the effectiveness of classical metho...

Construction of a diagnostic model for tuberculosis based on long non-coding RNA.

Annals of medicine
BACKGROUND: The World Health Organization encourages the development of novel diagnostic tools based on 'non-sputum' samples to meet global goals for tuberculosis (TB) control. We aimed to develop a machine learning-driven model for TB diagnosis, usi...

Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study.

JMIR medical informatics
BACKGROUND: There is a lack of venous thromboembolism (VTE) risk prediction models based on gene expression information. OBJECTIVE: This study aimed to construct a VTE prediction model based on whole blood gene expression profiling, by performing a c...

Identification of cell senescence-related genes in spontaneous preterm birth based on bioinformatics analysis and machine learning.

PloS one
Spontaneous premature birth (SPTB) is a common pregnancy complication; however, few studies have explored cell senescence-related markers in SPTB. Bioinformatics and machine learning approaches were used to predict potential biomarkers associated wit...

Machine learning and network pharmacology identify keloid biomarkers (AMPH, TNFRSF9) and therapeutic targets (IL6, HAS2) for aloe-derived quercetin.

PloS one
OBJECTIVE: This study aimed to identify diagnostic biomarkers for keloid and explore potential therapeutic agents from traditional Chinese medicine (TCM) by integrating network pharmacology approaches. Specifically, we sought to uncover key molecular...

Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning.

Molecular biomedicine
Acute myocardial infarction (AMI) remains a leading cause of global cardiovascular morbidity and mortality. Limitations in current diagnostic methods hinder early detection and intervention, creating an urgent need for novel early diagnostic biomarke...

Cell death-associated genes as novel diagnostic biomarkers for autism spectrum disorder.

Apoptosis : an international journal on programmed cell death
Autism spectrum disorder (ASD) involves neuroinflammation and dysregulated neuronal death but lacks objective diagnostic biomarkers. This study investigated whether cell death could serve as a molecular basis for ASD diagnosis. We identified cell dea...

Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...