dc.contributor.author |
Usié Chimenos, Anabel |
dc.contributor.author |
Karathia, Hiren |
dc.contributor.author |
Teixidó Torrelles, Ivan |
dc.contributor.author |
Alves, Rui |
dc.contributor.author |
Solsona Tehàs, Francesc |
dc.date |
2015-06-04T11:01:51Z |
dc.date |
2015-06-04T11:01:51Z |
dc.date |
2014 |
dc.identifier |
2167-8359 |
dc.identifier |
http://hdl.handle.net/10459.1/48308 |
dc.identifier |
https://doi.org/10.7717/peerj.276 |
dc.identifier.uri |
http://hdl.handle.net/10459.1/48308 |
dc.description |
One way to initiate the reconstruction of molecular circuits is by using automated
text-mining techniques. Developing more efficient methods for such reconstruction
is a topic of active research, and those methods are typically included by bioinfor-
maticians in pipelines used to mine and curate large literature datasets. Nevertheless,
experimental biologists have a limited number of available user-friendly tools that
use text-mining for network reconstruction and require no programming skills to
use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly
analysis of documents contained in a number of web-based literature databases to
identify co-occurrence of proteins/genes. This approach ensured results that were
always up-to-date with the latest live version of the databases. However, this `up-to-
dateness' came at the cost of large execution times. Here we report an evolution of
the application Biblio-MetReS that permits constructing co-occurrence networks
for genes, GO processes, Pathways, or any combination of the three types of entities
and graphically represent those entities.We show that the performance of Biblio-
MetReS in identifying gene co-occurrence is as least as good as that of other com-
parable applications (STRING and iHOP). In addition, we also show that the iden-
tification of GO processes is on par to that reported in the latest BioCreAtIvE chal-
lenge. Finally, we also report the implementation of a new strategy that combines
on-the-fly analysis of new documents with preprocessed information from docu-
ments that were encountered in previous analyses. This combination simultaneously
decreases program run time and maintains `up-to-dateness' of the results. |
dc.description |
RA was partially supported by the Ministerio de Ciencia e Innovación (MICINN, Spain through grant BFU2010-17704). FS was partially funded by the MICINN, with grants TIN2011-28689-C02-02. The authors are members of the research groups 2009SGR809 and 2009SGR145, funded by the “Generalitat de Catalunya”. AU is funded by a Generalitat de Catalunya (AGAUR) PhD fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
dc.language |
eng |
dc.publisher |
PeerJ |
dc.relation |
MICINN/PN2008-2011/BFU2010-17704 |
dc.relation |
MICINN/PN2008-2011/TIN2011-28689-C02-02 |
dc.relation |
Reproducció del document publicat a https://doi.org/10.7717/peerj.276 |
dc.relation |
PeerJ, 2014, núm. 2, pàg. 276-289 |
dc.rights |
cc-by, (c) Usié et al., 2014 |
dc.rights |
http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Network reconstruction |
dc.subject |
Systems biology |
dc.subject |
Literature analysis |
dc.title |
Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents |
dc.type |
article |
dc.type |
publishedVersion |