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Published on: October 31, 2013
Semantic annotation of biological concepts interplaying microbial cellular responses
Rafael Carreira1, Sónia Carneiro, Rui Pereira
1Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
BMC Bioinformatics
|November 30, 2011
Summary
This study introduces a new annotated corpus for Escherichia coli, detailing cellular responses to nutrient starvation. This resource aids biomedical text mining by identifying genes, proteins, and metabolites crucial for biological network analysis.
Area of Science:
- Systems Biology
- Biomedical Text Mining
- Computational Biology
Background:
- Automated extraction systems are crucial for Systems Biology, reducing manual effort in modeling and analyzing biological networks.
- Biomedical Text Mining tools face challenges in recognizing diverse biological concepts for these processes.
Purpose of the Study:
- To present a novel corpus for integrated cellular responses to nutrient starvation in Escherichia coli.
- To annotate a wide range of biomedical concepts involved in gene expression, regulation, and metabolism.
Main Methods:
- Development of a corpus annotating genes, DNA, RNA, proteins (transcription factors, enzymes, transporters), metabolites, physiological states, and lab techniques.
- Annotation of 130 full-text papers and 340 abstracts.
Main Results:
- The corpus contains 59,043 annotations for 3,649 unique biomedical concepts.
- Genes were the most frequent unique concepts, while compounds were the most frequently annotated concepts.
- Proteins constituted less than 10% of the annotated concepts.
Conclusions:
- This corpus is a unique resource for the text mining community, detailing a broad spectrum of biological concepts.
- Inter-annotator agreement highlights the need for a standardized background in complex biological text analysis.
- The corpus, annotation scheme, and guidelines are available for research purposes.
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