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| This is a more detailed description of our project proposal ''Semantic Network Summary'' (idea #6 in the GenMAPP list). |
== About == This is a more detailed description of our project proposal ''Semantic Network Summary'' (idea #6 in the GenMAPP list). <<BR>> |
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| '''Goal''' <<BR>> Develop a visual summary of a set of node attributes '''Description''' <<BR>> When biological networks are investigated, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To figure out the "biological meaning" of a cluster, the user has to sift through the long textual annotations that are associated to biological entities. We are interested in producing a graphical summary of such annotations. Word frequency in annotations is a good starting point. This can be visualized as a "tag cloud". In addition, the word layout can reflect similarity relations among words (e.g. co-occurrence in the same annotations). This functionality can be applied to several networks outside biology, whenever nodes are associated to verbose textual annotation. An example is professional social networks (e.g. linked-in), where individuals are "annotated" by a short CV. '''Language and Skills''' <<BR>> Java, basic statistics |
Google Summer of Code 2010: Semantic Network Summary
About
This is a more detailed description of our project proposal Semantic Network Summary (idea #6 in the GenMAPP list).
This project was started by
Short Description
Goal
Develop a visual summary of a set of node attributes
Description
When biological networks are investigated, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To figure out the "biological meaning" of a cluster, the user has to sift through the long textual annotations that are associated to biological entities. We are interested in producing a graphical summary of such annotations. Word frequency in annotations is a good starting point. This can be visualized as a "tag cloud". In addition, the word layout can reflect similarity relations among words (e.g. co-occurrence in the same annotations).
This functionality can be applied to several networks outside biology, whenever nodes are associated to verbose textual annotation. An example is professional social networks (e.g. linked-in), where individuals are "annotated" by a short CV.
Language and Skills
Java, basic statistics