SENT -- SEmantic features iN Text
What you can do:
Explore biology topics via literature analysis.
Highlights:
- SENT is a functional interpretation tool based on literature analysis.
- It uses Non-negative Matrix Factorization to identify topics in the scientific articles related to a collection of genes or their products, and use them to group and summarize these genes.
- It allows users to rank and explore the articles that best relate to the topics found, helping put the analysis results into context.
- This approach is useful as an exploratory step in the workflow of interpreting and understanding experimental data, shedding some light into the complex underlying biological mechanisms.
- This tool provides a user-friendly interface via a web site, and a programmatic access via a SOAP web server.
Keywords:
- literature mining
- text mining
Literature & Tutorials:
PubMed Link: SENT: semantic features in text
This record last updated: 09-01-2009