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Bipolar disorder Bipolar disorderBipolar disorder is a mental illness characterized by extreme mood swings and unusual shifts in a person’s energy, activity, and concentration levels. During manic episodes, individuals may feel euphoric, energetic, and require less sleep, while depressive phases are marked by low energy, sadness, indifference, or feelings of hopelessness. Bipolar disorder affects about 2% of the global population, with similar rates across both sexes. The onset of symptoms often occurs between the ages of 20-25 years, where an earlier onset is associated with a worse prognosis (Nierenberg AA et al. (2023)). Treatments for bipolar disorders involve the use of antipsychotics and mood stabilisers (Lane NM et al. (2023)). Despite the depressive aspect of the disease, antidepressants are not recommended unless they are combined with mood stabilisers or antipsychotics to avoid triggering manic episodes. Additionally, nonpharmaceutical approaches, such as pshychosocial and lifestyle interventions, have been found to improve symptoms and functionality. Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses, visualized through a volcano plot and summarized in the accompanying table for all three comparisons: 1) disease vs. all other diseases, 2) disease vs. healthy samples Disease vs All other
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.
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.
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
