Bioinformatics Vol. 17 no. 4 2001
Pages 359-363
© 2001 Oxford University Press
Original Paper |
Mining literature for protein–protein interactions
1 Molecular Biology Institute, UCLA-DOE
Laboratory of Structural Biology & Molecular Medicine,
University of California at Los Angeles, PO Box 951570, Los
Angeles, CA 90095-1570, USA
2 Protein Pathways Inc., 1145 Gayley Avenue,
Ste. 304, Los Angeles, CA 90024, USA
3 Institute of Cellular and Molecular
Biology, Department of Chemistry and Biochemistry, University of
Texas at Austin, 2500 Speedway, Austin, TX 78712, USA
Received on August 3, 2000
; revised on November 16, 2000
; accepted on November 22, 2000
Motivation: A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein–protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein–protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting Proteins. Furthermore, suitable gene annotations can also be captured.
Results: Our Bayesian approach scores Medline abstracts for
probability of discussing the topic of interest according to the
frequencies of discriminating words found in the abstract. More than
80 discriminating words (e.g. complex, interaction, two-hybrid) were
determined from a training set of 260 Medline abstracts
corresponding to previously validated entries in the Database of
Interacting Proteins. Using these words and a log likelihood scoring
function, 
2000 Medline abstracts were identified as
describing interactions between yeast proteins. This approach now
forms the basis for the rapid expansion of the Database of
Interacting Proteins.
Contact: marcotte{at}icmb.utexas.edu; ixenario{at}mbi.ucla.edu; david{at}mbi.ucla.edu
* These authors contributed equally to this work.
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