Summary:
SemNet is a series of modules for working with semantic networks. More specifically, we implemented algorithms that would help us gather features and rank nodes in terms of their connections to other nodes. The software was designed to work with a network of biomedical concepts stored in a locally hosted heterogenous information network. SemNet 2.0 expands on the first iteration through the investigation of new approximation algorithms and new data structures (getting rid of Neo4j, from the original SemNet, is a huge gain).
Team Leaders:
Stephen Allegri, David Kartchner, Anna Kirkpatrick
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