Biological Network
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A Biological Network is a Network that represents a set of Biological Relationships.
- Example(s):
- an Interactome, of molecular interactions.
- a Protein Interaction Network, of protein interactions.
- a Metabolic Pathway Network, of metabolic pathways.
- a Gene Regulation Network, of gene regulations.
- a Organic Neural Network, of an animal brain.
- …
- Counter-Example(s):
- See: Biological Process.
References
2013
- http://en.wikipedia.org/wiki/Biological_network
- A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical analysis of connections found in ecological, evolutionary, and physiological studies, such as neural networks.[1]
- ↑ Proulx, S. R.; Promislow, D. E. L.; Phillips, P. C. (2005). "Network thinking in ecology and evolution". Trends in Ecology and Evolution 20 (6): 345–353. doi:10.1016/j.tree.2005.04.004. PMID 16701391. http://eeb19.biosci.arizona.edu/Faculty/Dornhaus/courses/materials/papers/Proulx%20Promislow%20Phillips%20networks%20ecol%20evol.pdf.
- (Clune et al., 2013) ⇒ Jeff Clune, Jean-Baptiste Mouret, and Hod Lipson. (2013). “The Evolutionary Origins of Modularity.” In: Proceedings of the Royal Society of London B: Biological Sciences, 280(1755).
- QUOTE: A long-standing, open question in biology is how populations are capable of rapidly adapting to novel environments, a trait called evolvability [1]. A major contributor to evolvability is the fact that many biological entities are modular, especially the many biological processes and structures that can be modelled as networks, such as metabolic pathways, gene regulation, protein interactions and animal brains [1–7]. Networks are modular if they contain highly connected clusters of nodes that are sparsely connected to nodes in other clusters [4,8,9].
2009
- (Bilgic & Getoor, 2009) ⇒ Mustafa Bilgic, and Lise Getoor. (2009). “Reflect and Correct: A misclassification prediction approach to active inference.” In: ACM Transactions on Knowledge Discovery from Data (TKDD), 3(4). doi:10.1145/1631162.1631168
- Biological Networks. Where for example, the nodes represent proteins, attributes include annotations, and edges represent interactions. In this domain for example, we may be interested in inferring protein function.