Mining Protein-Protein Interactions from Literature
Author Information
Author(s): Huang Minlie, Ding Shilin, Wang Hongning, Zhu Xiaoyan
Primary Institution: Tsinghua University
Hypothesis
There is a need for effective text-mining tools to extract physical protein-protein interactions from the literature.
Conclusion
The study presents a text-mining framework that effectively extracts physical protein-protein interactions, ranking among the top performers in the BioCreative 2006 evaluation.
Supporting Evidence
- The method achieved a precision of 75.07% and a recall of 81.07% in filtering irrelevant articles.
- In identifying protein mentions, the method had a precision of 34.83% and a recall of 24.10%.
- The profile-based method was competitive, achieving a precision of 36.95% on the SwissProt-only subset.
Takeaway
This study created a tool that helps scientists find out how proteins interact with each other by reading lots of scientific papers quickly.
Methodology
The study used a text-mining framework that included article filtering, protein mention identification, normalization to molecule identifiers, and extraction of protein-protein interactions.
Potential Biases
The reliance on training data that may not represent the full range of literature can introduce bias.
Limitations
The method struggles with protein name normalization and requires evidence to be present in the same sentence for interaction extraction.
Statistical Information
P-Value
0.02
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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