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SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells
Published on: March 3, 2017
Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database
Daniel G Jamieson1, Martin Gerner, Farzaneh Sarafraz
1Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK.
Database : the Journal of Biological Databases and Curation
|April 25, 2012
Summary
Text mining (TM) can efficiently recreate biological databases like the HIV-1, human protein interaction database (HHPID). This automated approach significantly enhances data extraction from scientific literature, aiding manual curation efforts.
Area of Science:
- Bioinformatics
- Computational Biology
- Text Mining
Background:
- Manual curation of biological databases, such as the HIV-1, human protein interaction database (HHPID), is time-consuming and resource-intensive.
- Advancements in text mining (TM) offer potential for automated, accurate data extraction from large scientific literature volumes.
- The HHPID currently contains 2589 manually extracted interactions from 3090 articles.
Purpose of the Study:
- To recreate the HHPID using state-of-the-art text mining techniques.
- To evaluate the accuracy and efficiency of TM in extracting protein-protein interactions from scientific abstracts and full-text articles.
- To compare TM-extracted data with manually curated data in the HHPID.
Main Methods:
- Performed gene/protein named entity recognition (NER) on abstracts and titles cited in the HHPID.
- Applied two molecular event extraction tools to identify protein interactions.
- Analyzed 49 open-access full-text articles to compare TM extraction from abstracts versus full texts.
Main Results:
- Achieved high scores for NER (88.6% F-score) and event extraction (80.1% F-score).
- Successfully recreated over 50% of HHPID interactions from abstracts and titles alone.
- Extracted 31% more unique HIV-1-human interactions from full-text articles compared to the HHPID (237 vs. 187).
- TM extracted 23 times more interaction mentions and 6 times more unique interactions from full texts than from abstracts/titles.
Conclusions:
- Text mining is a powerful tool for reconstructing and expanding biological databases like the HHPID.
- TM significantly enhances the efficiency and scope of biological data curation, serving as a valuable assistant to manual efforts.
- The study identified additional HIV-1 interactions not currently in the HHPID, demonstrating TM's ability to broaden data scope.
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