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Viral hepatitis related cirrhosisViral hepatitis related cirrhosisHepatitis is most commonly caused by infections from the viruses known as hepatitis A, B, C, D, or E. These viral infections stem either from consuming food and/or drinks contaminated with fecal matter (hepatitis A and E) or from contact with infected biofluids (hepatitis B, C, and D) (WHO). Excluding hepatitis A, which does not tend to cause long-term liver damage, infections from the other four viruses are some of the main causes of chronic liver disease (CLD) worldwide (Sharma A et al. (2024)). This is more pronounced in East Asia and Sub-Saharan Africa, where chronic hepatitis B, C, and D infections are the most common cause of CLD. While hepatitis B tends to be a short-term disease, it can lead to cirrhosis, cancer, and liver failure when it becomes chronic. As opposed to hepatitis B, hepatitis C progresses into chronic disease in most cases. If not adequately treated, chronic hepatitis C may lead to hepatocellular carcinoma. The least common of all, hepatitis D is a satellite virus which only propagates togehter with hepatitis B. This co-infection can have severe complications, with an increased risk of chronically developing cirrhosis and liver cancer. While vaccination is the best prevention strategy for hepatitis B (Nelson NP et al. (2016)), there is currently no effective vaccine against hepatitis C or D. However, with timely diagnosis, antiviral therapy for hepatitis B, C, or D can hinder disease progression and increase survival chances. The most common way to diagnose hepatitis B, C, and D is through serological assays where either the viral antigen or the antibodies produced against it are detected (Nelson NP et al. (2016)). 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
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