A user-friendly web application that contains ready-for-simulation versions of the BioModels Database, and allows for the intuitive creation of new models. Experimental biologist and students of bioinformatics or systems biology without programming skills can easily use it. Expert users can quickly implement basic models and downloading the code for further tailoring. 

EasyModel: user-friendly tool for building and analysis of simple mathematical models in systems biology. Bartolome J, Alves R, Solsona F, Teixido I. Bioinformatics. 2019 Aug 26

MetReS, an Efficient Database for Genomic Applications.

MetReS (Metabolic Reconstruction Server) is a genomic database that is shared between two software applications that address important biological problems. Biblio-MetReS is a data-mining tool that enables the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the processes of interest and their function.

MetReS, an Efficient Database for Genomic Applications. Vilaplana J, Alves R, Solsona F, Mateo J, Teixidó I, Pifarré M. J Comput Biol. 2018 Feb;25(2):200-213. doi: 10.1089/cmb.2017.0103. Epub 2017 Nov 29.



Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents. Usie A, Karathia H, Teixidó I, Alves R, Solsona F. PeerJ. 2014 Feb 27;2:e276. doi: 10.7717/peerj.276. eCollection 2014.

Biblio-MetReS: a bibliometric network reconstruction application and server. Usié A, Karathia H, Teixidó I, Valls J, Faus X, Alves R, Solsona F. BMC Bioinformatics. 2011 Oct 5;12:387. doi: 10.1186/1471-2105-12-387.


Presents a valid alternative for automated annotation of chemical entities in biomedical documents. The individual performance of CheNER could be further improved by expanding the dictionaries of chemical entities used in its training. In addition, CheNER may provide a valuable resource to automatically derive new features that could be used for training and improving the performance of newer methods for tagging chemical entities.

CheNER: a tool for the identification of chemical entities and their classes in biomedical literature. Usié A, Cruz J, Comas J, Solsona F, Alves R. J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S15. doi: 10.1186/1758-2946-7-S1-S15. eCollection 2015.

CheNER: chemical named entity recognizer. Usié A, Alves R, Solsona F, Vázquez M, Valencia A. Bioinformatics. 2014 Apr 1;30(7):1039-40. doi: 10.1093/bioinformatics/btt639. Epub 2013 Nov 13.