Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39418

TitleReconstructing transcriptional regulatory networks using data integration and text mining
Author(s)Pereira, Rafael T.
Costa, Hugo
Carneiro, S.
Rocha, Miguel
Mendes, Rui
Issue date2015
PublisherIEEE
JournalIeee International Conference on Bioinformatics and Biomedicine-Bibm
CitationPereira, R.; Costa, H.; Carneiro, S.; Rocha, Miguel; Mendes, Rui, Reconstructing transcriptional regulatory networks using data integration and text mining. BIBM 2015 - IEEE International Conference on Bioinformatics and Biomedicine. Washington D.C., USA, Nov 9-12, 1552-1558, 2015.
Abstract(s)Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.
TypeConference paper
URIhttp://hdl.handle.net/1822/39418
ISBN9781467367981
DOI10.1109/BIBM.2015.7359907
ISSN2156-1125
Publisher versionhttps://cci.drexel.edu/ieeebibm/bibm2015/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Artigos em Livros de Atas / Papers in Proceedings

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