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Nucleic Acids Research Advance Access originally published online on May 20, 2009
Nucleic Acids Research 2009 37(Web Server issue):W153-W159; doi:10.1093/nar/gkp392
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Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W153-W159
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

SENT: semantic features in text

Miguel Vazquez1, Pedro Carmona-Saez2, Ruben Nogales-Cadenas2, Monica Chagoyen3, Francisco Tirado2, Jose Maria Carazo3 and Alberto Pascual-Montano2,*

1Software Engineering Department, 2Computer Architecture Department, Complutense University and 3Biocomputing Unit, National Center for Biotechnology, CNB-CSIC, Madrid, Spain

*To whom correspondence should be addressed. Tel: +34 913 944 420; Fax: +34 913 944 687; Email: pascual{at}fis.ucm.es

Received January 31, 2009. Revised April 20, 2009. Accepted April 30, 2009.

We present SENT (semantic features in text), a functional interpretation tool based on literature analysis. SENT 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. In addition, the application 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. SENT is freely accessible at http://sent.dacya.ucm.es.


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