We are interested in the understanding of the mechanisms underlying biomedical related problems at the molecular scale. This involves the study of the network of interactions between molecules that underly the etiology of human geentic diseases. In addition, we are also interested in the mechanisms underlying the appearance of side effects after drug treatments.
One part of our research is focused in strategies to, once a network of molecular interactions is obtained, characterize the network and model its behavior in order to gain insight into the etiology of the disease phenotype. In particular, we are interested in the application of qualitative modeling approaches, such as Petri Nets and Boolean networks.
Another line of research involves strategies for obtaining the networks that are relevant for the biomedical related problems already mentioned. One example of this is DisGeNET. DisGeNET is both a database of gene-disease associations and a Cytoscape plugin to work with a network representation of the database. DisGeNET integrates information about genes for mendelian, complex and environmental diseases, and constitutes the largest public resource containing this information that is publicly available. In addition, we are developing software for the retrieval and analysis of data about biological pathways from public network repositories and their subsequent integration with genomic information, with the final goal of modelling the effect of sequence variations on the dynamics of a biologic process. Although the publicly available network databases contain valuable information, we are aware that their coverage is not complete: a lot of information regarding interaction between biomedical entities (genes, proteins, phenotypes, chemicals, drugs, etc) still lyes in the biomedical literature as free text.
Here comes our third line of research, which involves the use of text mining approaches for the extraction of relationships between biomedical entities from the biomedical literature. In the past years we have developed NER systems for the identification of mentions of gene sequence variants from MEDLINE abstracts, and linkage of the mentions found in text to the corresponding database identifiers (in this case dbSNP). In addition, we have developed a corpus with annotations for variation mentions for the evaluation of this kind of NER systems. Currently, we are working on the application of NLP approaches for the identification and extraction of different types of relationships between biomedical entities.
Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces and adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs. Learn more about ADR substantiation here.
DisGeNET is a plugin for Cytoscape to query and analyze a network representation of human gene-disease databases. For this purpose, we have developed a new gene-disease database integrating data from several public sources. DisGeNET allows user-friendly access to our database, which includes queries restricted to (i) the original data source, (ii) the association type, (iii) the disorder class of interest and (iv) specific diseases, respectively genes. It represents gene-disease associations in terms of bipartite graphs and additionally provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of human complex diseases with respect to their genetic origin by a variety of built-in functions. Moreover, DisGeNET permits multicoloring of nodes (genes/diseases) according to their disease classes for expedient visualization. DisGeNET is distributed under the GNU GPL licence. Go to DisGeNET web now!
The new implementation of OSIRIS (OSIRISv1.2) incorporates a new entity recognition module and is built on top of a local mirror of MEDLINE collection and HgenetInfoDB. HgenetInfoDB is a database that integrates data of human genes from the NCBI Gene database and dbSNP. The entity recognition module is based on a corpus of articles annotated with gene identifiers and the new search algorithm, which uses a pattern-based search strategy and a sequence variant nomenclature dictionary for the identification of terms denoting SNPs and other sequence variants and their mapping to dbSNP entries. The use of OSIRISv1.2 generates a corpus of annotated literature linked to sequence database entries (NCBI Gene and dbSNP). The results of the searches are stored in a database that can be used to query the results and, in the future, for the extraction of relationships among biological entities. The performance of OSIRISv1.2 was evaluated on a manually annotated corpus, resulting in a 99 % precision at a 82 % recall, and a F-score of 0.9. Results on two sets of genes, one related to the disease cerebral aneurysm and the other to breast cancer, are currently available.
The database HgenetInfoDB is a repository of Homo sapiens genes and their sequence variants.
Visit this link for a description of the available corpora.
Bauer-Mehren A, van Mullingen EM, Avillach P, Carrascosa MC, Garcia-Serna R, Piñero J, Singh B, Lopes P, Oliveira JL, Diallo G, Mestres J, Ahlberg Helgee E, Boyer S, Sanz F, Kors JA, Furlong LI. Automatic filtering and substantiation of drug safety signals, to appear in Plos Comp Biol
Anna Bauer-Mehren, Markus Bundschus, Michael Rautschka, Miguel A. Mayer, Ferran Sanz, Laura I. Furlong. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS ONE 2011 6(6): e20284. doi:10.1371/journal.pone.0020284. Link
Philippe E. Thomas, Roman Klinger , Laura I. Furlong , Martin Hofmann-Apitius and Christoph M. Friedrich. Challenges in the Association of Human Single Nucleotide Polymorphism Mentions with Unique Database Identifiers. BMC Bioinformatics 2011, 12(Suppl 4):S4
Dietrich Rebholz-Schuhmann, Antonio Jimeno Yepes, Chen Li, Senay Kafkas,Ian Lewin, Ning Kang, Peter Corbett, David Milward, Ekaterina Buyko,Elena Beisswanger, Kerstin Hornbostel, Alexandre Kouznetsov, René Witte, Jonas B. Laurila, Christopher J.O. Baker, Chen-Ju Kuo, Simone Clematide, Fabio Rinaldi, Richárd Farkas, György Móra, Kazuo Hara, Laura Furlong,Michael Rautschka, Mariana Lara Neves, Alberto Pascual-Montano, Qi Wei,Nigel Collier, Md. Faisal Mahbub Chowdhury, Alberto Lavelli, Rafael Berlanga, Roser Morante, Vincent Van Asch, Walter Daelemans, José Luís Marina, Erik van Mulligen, Jan Kors, Udo Hahn. Assessment of NER solutions against the first and second CALBC Silver Standard Corpus. Accepted for publication in the SMBM 2010 special issue in the Journal of Biomedical Semantics.
Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI. DisGeNET - a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics. 2010 Nov 15;26(22):2924-6. Epub 2010 Sep 21. Link
Bauer-Mehren A, Furlong LI, Sanz F. Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol. 2009;5:290. Link
Bauer-Mehren A, Furlong LI, Sanz F. From SNPs to pathways: Integration of functional effect of sequence variations on models of cell signalling pathways. BMC Bioinformatics 2009, 10(Suppl 8):S6 Link
Furlong L.I. and Sanz F. Identification of sequence variants of genes from biomedical literature: the OSIRIS approach. Book chapter for the book "Information Retrieval for Biomedicine: Natural Language Processing for Knowledge Integration", Violaine Prince and Mathieu Roche (Eds), IGI Global, 2009. Chapter pre-print
Hofmann-Apitius M, Fluck J, Furlong L, Fornes O, Kolarik C, Hanser S, Boeker M, Schulz S, Sanz F, Klinger R, Mevissen T, Gattermayer T, Oliva B, Friedrich CM. Knowledge environments representing molecular entities for the virtual physiological human. Philos Transact A Math Phys Eng Sci. 2008 Jun 17. Link
Furlong LI, Dach H, Hofmann-Apitius M, Sanz F. OSIRISv1.2: a named entity recognition system for sequence variants of genes in biomedical literature. BMC Bioinformatics 2008, 9:84. Link
Klinger R, Friedrich CM, Mevissen HT, Fluck J, Hofmann-Apitius M, Furlong LI, Sanz F. Identifying gene-specific variations in biomedical text. J Bioinform Comput Biol. 2007 Dec;5(6):1277-96. Link
Bonis J, Furlong LI, Sanz F. OSIRIS: a tool for retrieving literature about sequence variants. Bioinformatics. 2006 Oct 15;22(20):2567-9. Epub 2006 Jul 31. Link
27/06/2011 Biomedicina Computacional y efectos adversos de los medicamentos (Diario Médico) Link
13/07/2011 La bioinformàtica permet tenir un coneixement modular de les malalties genètiques Link
The Integrative Biomedical Informatics Group promotes and tackles the synergistic and integrative approaches of the diverse reasearch lines developed by the research groups of the Research Unit on Biomedical Informatics (GRIB). The GRIB hosts the Node of Biomedical Informatics of the INB. The Integrative Biomedical Informatics Group group focuses on the application of methods and software developed in-house to tackle human health issues, including disease prevention and diagnosis and therapeutic tecnologies. One of our research lines is devoted in the development of new strategies and tools for text mining, focused in the literature retrieval and named entity recognition, particularly considering the documents dealing with genetic variations.
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TopComments and suggestions: Laura I. Furlong (lfurlong@imim.es) Integrative Biomedical Informatics Group, Research Unit on Biomedical Informatics (GRIB), Institut Municipal d´Investigació Médica (IMIM) and Universitat Pompeu Fabra (UPF).
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Updated: March 2012
by Laura I. Furlong (visit my web page)