Dataset Information

A realistic assessment of methods for extracting gene/protein interactions from free text.

ABSTRACT:

Background

The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.

Results

Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions.

Conclusion

In terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community.

SUBMITTER: Kabiljo R

PROVIDER: S-EPMC2723093 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Publications

A realistic assessment of methods for extracting gene/protein interactions from free text.

Kabiljo Renata R Clegg Andrew B AB Shepherd Adrian J AJ

BMC bioinformatics 20090728

<h4>Background</h4>The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger. ...[more]

PMID: 19635172

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Dataset Information

A realistic assessment of methods for extracting gene/protein interactions from free text.

Background

Results

Conclusion

Publications

A realistic assessment of methods for extracting gene/protein interactions from free text.

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