SENT: semantic features in text. Export

@article{citeulike:4632803, abstract = {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.}, author = {Vazquez, Miguel and Carmona-Saez, Pedro and Nogales-Cadenas, Ruben and Chagoyen, Monica and Tirado, Francisco and Carazo, Jose Maria M. and Pascual-Montano, Alberto}, citeulike-article-id = {4632803}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/nar/gkp392}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19458159}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19458159}, day = {1}, doi = {10.1093/nar/gkp392}, issn = {1362-4962}, journal = {Nucleic acids research}, keywords = {data-interpretation, literature-based-annotation, nlp, unsupervised-learning}, month = {July}, number = {Web Server issue}, pages = {gkp392+}, posted-at = {2009-10-13 06:15:21}, priority = {2}, title = {SENT: semantic features in text.}, url = {http://dx.doi.org/10.1093/nar/gkp392}, volume = {37}, year = {2009} }

TY - JOUR ID - citeulike:4632803 L3 - citeulike-article-id:4632803 N2 - 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. IS - Web Server issue JF - Nucleic acids research SN - 1362-4962 TI - SENT: semantic features in text. VL - 37 SP - gkp392 KW - data-interpretation KW - literature-based-annotation KW - nlp KW - unsupervised-learning AU - Vazquez, Miguel AU - Carmona-Saez, Pedro AU - Nogales-Cadenas, Ruben AU - Chagoyen, Monica AU - Tirado, Francisco AU - Carazo, Jose Maria AU - Pascual-Montano, Alberto PY - 2009/07/01/ UR - http://dx.doi.org/10.1093/nar/gkp392 ER -