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Influenza InfluenzaInfluenza, commonly known as the flu, is an acute respiratory infection caused by influenza viruses, primarily types A and B, which are responsible for seasonal epidemics worldwide. Influenza viruses belong to the Orthomyxoviridae family and have a negative-sense, single-stranded RNA genome (Gaitonde DY et al. (2019)). The virus spreads easily through respiratory droplets when infected individuals cough or sneeze. Typical symptoms include a sudden onset of fever, cough, sore throat, body aches, and fatigue. Symptoms usually begin 1–4 days after infection and last about a week. Most people recover without medical interventions, but severe cases, particularly in individuals with risk factors, may require antiviral treatment and hospitalization. The pathogenesis of influenza involves the virus infecting respiratory epithelial cells (Wu NH et al. (2016)). The virus binds to sialic acid receptors on the host cells, leading to entry, replication, and the release of new virus particles. This infection triggers an immune response, where cytokines and chemokines attract inflammatory cells, such as neutrophils and macrophages, to the site. Severe infections can cause complications like acute respiratory distress syndrome (ARDS) and secondary bacterial infections, with both direct viral damage and an overactive immune response contributing to severe outcomes. 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 Project
The Human Protein Atlas
The Human Protein Atlas project is funded
