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2026 Systems Immunology Course Pre-Test
Unless otherwise stated, please answer each question to the best of your ability.
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1.
What are some major bottlenecks in translating discoveries from human genetics into mechanistic or therapeutic insight?
(Required.)
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2.
Why are both regulatory genomics and functional experimental data needed to interpret many GWAS loci?
(Required.)
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3.
In what way single-cell analysis has changed our view of the immune system and what’s the translational impact?
(Required.)
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4.
What is a SNP and how SNP analysis can inform the identification of disease-driver genes?
(Required.)
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5.
What are the driving principles of data clustering and what is it useful for?
(Required.)
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6.
How can systems immunology drive the discovery on novel therapeutic targets?
(Required.)
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7.
What is the difference between modeling a simple physical system, such as a spring-mass system, and modeling a biological system?
(Required.)
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8.
Why might longitudinal measurements and using individuals as their own controls be especially important in systems immunology?
(Required.)
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9.
What are some reasons why increasing biomedical data, computing power, and R&D investment have not automatically solved the problem of drug development productivity?
(Required.)
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10.
Why is the choice of similarity measure or distance function important when analyzing multi-omics data?
(Required.)
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11.
How do PCA, ISOMAP, t-SNE, and UMAP differ conceptually as tools for visualizing high-dimensional biological data?
(Required.)
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12.
What is the danger of performing exploratory statistical testing, such as ANOVA, across thousands of variables without proper correction or validation?
(Required.)
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13.
In the framework of annotated measurement maps, how do annotation space, measurement space, geometry, and robustness come together to define useful biomarkers?
(Required.)
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14.
What is data anyway?
(Required.)
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15.
What data processing technique connects early heuristic sequence search algorithms with modern biological foundation models?
(Required.)
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16.
What are the main differences between biological foundation models when applied to different data types?
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17.
Why should we not abandon traditional machine learning research in favor of foundation models?
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18.
Why is attention "everything," and when can it become problematic?
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19.
How might AI negatively impact healthcare research and what steps can we take to mitigate these risks?
(Required.)
