AIMC Topic: Mendelian Randomization Analysis

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Identification of biomarkers for endometriosis based on summary-data-based Mendelian randomization and machine learning.

Medicine Apr 4, 2025
Endometriosis (EM) significantly impacts the quality of life, and its diagnosis currently relies on surgery, which carries risks and may miss early lesions. Noninvasive biomarkers are urgently needed for early diagnosis and personalized treatment. Th...
Humans Machine Learning Biomarkers Female Genome-Wide Association Study Mendelian Randomization Analysis Endometriosis
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Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.

Thoracic cancer Mar 1, 2025
BACKGROUND: Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate the influence of lipid metabolism-related genes (LMRGs) on the diagnosis and treatment of LUAD and to...
Humans Machine Learning Biomarkers, Tumor Lung Neoplasms Mendelian Randomization Analysis Lipid Metabolism Adenocarcinoma of Lung
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Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

International journal of chronic obstructive pulmonary disease Jan 1, 2025
BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and th...
Humans Machine Learning Biomarkers Pulmonary Disease, Chronic Obstructive Molecular Docking Simulation Gene Expression Profiling Sepsis Genome-Wide Association Study Gene Regulatory Networks Genetic Predisposition to Disease Computational Biology Predictive Value of Tests Mendelian Randomization Analysis
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Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

Medicine Jul 19, 2024
This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression networ...
Humans Machine Learning Biomarkers Sepsis ROC Curve Computational Biology Nomograms Mendelian Randomization Analysis
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