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Previous venous thromboembolismPrevious venous thromboembolismVenous thromboembolism (VTE) is a common and complex cardiovascular disorder with serious short- and long-term complications, comprising both deep vein thrombosis (DVT) and pulmonary embolism (PE). VTE is a cardiovascular event where a thrombus or blood clot is formed, usually in the deep veins of the lower limbs of the body, causing disruption to the blood flow, leading to inflammation and leg ulcers. If the thrombus dislodges, it creates a so-called embolus, which can travel to the lungs. Here, if left untreated, it has a mortality rate of ~30% (Søgaard KK et al. (2014)). Common symptoms of VTE can include swollen and tender legs, as well as shortness of breath and pain when breathing. There are numerous risk factors. Most blood-clots occur during or soon after a hospital stay or surgery. Other risk factors include being immobile, older age, high BMI, family history of VTE, recent or recurrent cancer, pregnancy, estrogen-based medicine or in relation to an injury and trauma. Currently, VTE affects over 10 million people globally each year. Elevated levels of CRP and D-dimer are positively predictive of VTE. In the clinics, DVT is confirmed through compression ultrasound, and PE is confirmed through diagnostic imaging, such as computer pulmonary angiography (CTPA). Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses. The analyses are reported for three comparisons: 1) disease vs. all other diseases, 2) disease vs. diseases from the same class, and 3) disease vs. healthy samples. Disease vs All other
Disease vs Class
Disease vs Healthy
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
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The Human Protein Atlas
The Human Protein Atlas project is funded
