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MalariaMalariaMalaria is a life-threatening disease caused by parasites of the Plasmodium genus, transmitted through the bite of an infected mosquito (Buck E et al. (2024)). Of the species that infect humans, P. falciparum is the most prevalent and dangerous, leading to the most severe and fatal forms of the disease. Infection begins when a mosquito injects sporozoites into the bloodstream (Milner DA. (2018)). These sporozoites travel to the liver, where they multiply and form merozoites, which are later released into the bloodstream. Once in circulation, merozoites invade red blood cells, progressing through several developmental stages. The eventual rupture of these cells releases more parasites, continuing the infection cycle and causing symptoms. Key pathogenic mechanisms involve the direct destruction of red blood cells, leading to hemolysis, and the immune system's inflammatory response, resulting in systemic symptoms. Infected red blood cells can also accumulate in small blood vessels, leading to issues like cerebral malaria. Symptoms of malaria, such as fever, headache, nausea, vomiting, muscle aches, and chills, are non-specific and may resemble other illnesses. In severe cases, particularly with P. falciparum, complications may include coma, respiratory distress, renal failure, severe anemia, and multiorgan failure. Because of its non-specific presentation, malaria should be considered in any patient with fever with a recent travel history to malaria-endemic regions. 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
