Difference between revisions of "User talk:The Facebook of genes: community identification in biological networks"
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| − | Background: In biology, many processes involve a group of genes that are expressed at the same time or a group of proteins that interact with each other. These interactions can be represented as networks, and modules, which are groups of densely interconnected nodes (i.eg. genes or proteins), can be identified. A module should regroup nodes that work in the same pathway or have a related function, thus allowing to understand better different biological processes, pathways and causes of disease. | + | '''Background:''' In biology, many processes involve a group of genes that are expressed at the same time or a group of proteins that interact with each other. These interactions can be represented as networks, and modules, which are groups of densely interconnected nodes (i.eg. genes or proteins), can be identified. A module should regroup nodes that work in the same pathway or have a related function, thus allowing to understand better different biological processes, pathways and causes of disease. |
| − | Goal: To identify overlapping and non-overlapping modules in different biological networks. | + | '''Goal:''' To identify overlapping and non-overlapping modules in different biological networks. |
| − | Computational tools: For this project, we used the linkcomm R package to identify overlapping modules. We also created an algorithm to transform the results into a list of non-overlapping modules. | + | '''Computational tools:''' For this project, we used the ''linkcomm'' R package to identify overlapping modules. We also created an algorithm to transform the results into a list of non-overlapping modules. |
| − | Keywords: community identification, biological networks, pathways | + | '''Keywords:''' community identification, biological networks, pathways |
| − | Supervisor: Daniel Marbach | + | '''Supervisor:''' Daniel Marbach |
Revision as of 18:19, 6 June 2017
Background: In biology, many processes involve a group of genes that are expressed at the same time or a group of proteins that interact with each other. These interactions can be represented as networks, and modules, which are groups of densely interconnected nodes (i.eg. genes or proteins), can be identified. A module should regroup nodes that work in the same pathway or have a related function, thus allowing to understand better different biological processes, pathways and causes of disease.
Goal: To identify overlapping and non-overlapping modules in different biological networks.
Computational tools: For this project, we used the linkcomm R package to identify overlapping modules. We also created an algorithm to transform the results into a list of non-overlapping modules.
Keywords: community identification, biological networks, pathways
Supervisor: Daniel Marbach
