18 Questions
What is a key feature of scale-free networks?
Small-world effect due to hubs
Why is identifying interactions or correlations important in causality?
To establish a causal mechanism
How can outside knowledge contribute to network sparsification?
Aid in extracting most likely subnetworks
What does the onion structure in networks allow for?
Multi-level responses
In protein modeling, what is the significance of learning amino acid codes?
Learning binding preferences of different proteins
Why do scale-free networks create a small-world effect?
Presence of hubs reducing path lengths
What type of interactions involve stable interactions such as ribosomes, proteasome, and respiratory chain complexes?
Protein-protein stable interactions
Which centrality measure refers to the number of shortest paths that use a node in a network?
Betweenness centrality
What type of network analysis measure is most commonly used for guilt-by-association analyses like predicting proteins as cancer drivers?
Degree centrality
In network analysis, what does network density refer to?
The number of edges compared to the maximum possible edges
Which type of network interaction involves post-translational modifications and steric hindrance?
Protein-protein transient interactions
What is the reciprocal of the average shortest path distance to all other nodes in a network called?
Closeness centrality
How does network centrality influence gene retention or loss?
Influences evolutionary retention or loss
What impact does alternative splicing have on interactome complexity?
Increases interactome complexity
How do Mendelian disease mutations impact protein-protein interactions (PPIs)?
Lead to variations in disease severity
What contributes to organismal complexity and functional diversity according to the text?
Alternative splicing
How can the effects of mutations on disease be predicted according to the text?
Using in-silico methods incorporating structural and network data
What does alternative splicing reveal within a network, according to the text?
Deeper modularity and isoform-level resolution
Explore the properties and predictions of complex biological systems through network simulations. Learn about different types of biological networks like protein-protein interactomes and stable interactions such as ribosomes and cytoskeleton. Understand how incorrect predictions are attributed to modeling errors or lack of detail.
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