title: Semantic annotation of biological concepts interplaying microbial cellular responses.
abstract: BACKGROUND
  Automated 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.
  
  
  RESULTS
  Here, 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.
  
  
  CONCLUSIONS
  To 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.
authors: Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
authors: Rafael
authors: R
authors: Carreira
authors: Sónia
authors: S
authors: Carneiro
authors: Rui
authors: R
authors: Pereira
authors: Miguel
authors: M
authors: Rocha
authors: Isabel
authors: I
authors: Rocha
authors: Eugénio C
authors: EC
authors: Ferreira
authors: Anália
authors: A
authors: Lourenço
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D057225:Data Mining; D004926:Escherichia coli / Q000166:cytology* / Q000378:metabolism*; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D012984:Software; D018875:Vocabulary, Controlled
chemical_list: D001426:Bacterial Proteins