Semantic annotation of biological concepts interplaying microbial cellular responses

BMC Bioinformatics

Research Article

Miguel Rocha¹  Isabel Rocha²  Eugénio C Ferreira²  Anália Lourenço²  Sónia Carneiro²  Rui Pereira²  Rafael Carreira³ 

[1] Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057, Braga, PORTUGAL;IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057, Braga, PORTUGAL;IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057, Braga, PORTUGAL;Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057, Braga, PORTUGAL;

关键词: Text Mining;    Final Corpus;    Annotation Scheme;    Biological Concept;    Stringent Response;   

DOI  :  10.1186/1471-2105-12-460

 received in 2011-05-18, accepted in 2011-11-28,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundAutomated extraction systems have become a time saving necessity in Systems Biology. Considerable human effort is needed to model, analyse and simulate biological networks. Thus, one of the challenges posed to Biomedical Text Mining tools is that of learning to recognise a wide variety of biological concepts with different functional roles to assist in these processes.ResultsHere, we present a novel corpus concerning the integrated cellular responses to nutrient starvation in the model-organism Escherichia coli. Our corpus is a unique resource in that it annotates biomedical concepts that play a functional role in expression, regulation and metabolism. Namely, it includes annotations for genetic information carriers (genes and DNA, RNA molecules), proteins (transcription factors, enzymes and transporters), small metabolites, physiological states and laboratory techniques. The corpus consists of 130 full-text papers with a total of 59043 annotations for 3649 different biomedical concepts; the two dominant classes are genes (highest number of unique concepts) and compounds (most frequently annotated concepts), whereas other important cellular concepts such as proteins account for no more than 10% of the annotated concepts.ConclusionsTo the best of our knowledge, a corpus that details such a wide range of biological concepts has never been presented to the text mining community. The inter-annotator agreement statistics provide evidence of the importance of a consolidated background when dealing with such complex descriptions, the ambiguities naturally arising from the terminology and their impact for modelling purposes.Availability is granted for the full-text corpora of 130 freely accessible documents, the annotation scheme and the annotation guidelines. Also, we include a corpus of 340 abstracts.

【 授权许可】

Unknown   
© Carreira et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.