Data and text mining Wnt pathway curation using automated natural language processing : combining statistical methods with partial and full parse for knowledge extraction

Motivation: Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. Results: A ‘gold standard’ set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford. edu/∼rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peerreviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the ‘gold standard’ Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wntrelated molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human annotation and maintenance of signal pathway databases. Availability: The pipeline software components are freely available on request to the authors. Contact: dstates@umich.edu Supplementary information: http://stateslab.bioinformatics. med. umich.edu/software.html ∗To whom correspondence should be addressed. INTRODUCTION Detailed signal pathway annotation and model construction can be an arduous task for human readers to accomplish. The task is complicated for heavily investigated pathways like the Wnt signal transduction cascade or other major cellular pathways due to the large volume of papers published for biological interactions involving members of those pathways. In the Wnt signal transduction literature, for instance, there were 239 MeSH-annotated ‘Signal Transduction’ Wnt pathway MEDLINE articles in 2003, and 889 articles for the period from 2000 to 2004. Expanding the search to include other co-factors or major proteins in the pathway expands the results to many thousands of articles. For a pathway like the Wnt pathway, up-to-date models are essential for investigators in the field; without accurate models, experimental results may be placed outside of the proper biological context or key insights may be missed altogether if the model structure is incorrect. Comprehensively annotated models of complex pathways like Wnt are also essential for hypothesis-generation and experiment validation, yet with the exception of periodic reviews on the subject, there are few sources of Wnt-signaling information that are kept consistent with the latest published literature. In the past, various groups (Andrade and Valencia, 1997; Blaschke, 1999; Daraseliaet al., 2004; Iliopouloset al., 2001; Koikeet al., 2003; Raychaudhuri et al., 2002; Stephens et al., 2001; Wilbur and Yang, 1996) have used natural language processing (NLP) systems to extract biological molecule annotation information (Andrade and Valencia, 1997), to detect protein–protein interaction information (Bader and Hogue, 2002; Blaschke, 1999; Marcotte e al., 2001), or to improve indexing and recall into searches from MEDLINE abstracts (Iliopoulos et al., 2001; Stapley and Benoit, 2000). Methods included a mixture of text mining and indexing, with some groups using classification by Bayesian statistics (Wilbur and Yang, 1996), structured grammar matches (Temkin and Gilder, 2003), or word filtering of known entities, as well as the use of partial and full parsers. Full parsers have been employed to discover protein–protein interactions with promising results, highlighting the utility of this approach (Daraselia et al., 2004); however they are not available as open-source. We have developed an automated NLP-based system to assist in the generation of up-to-date pathway models from the literature that can automatically detect and rank key interacting proteins in an article corpus like that of Wnt signaling. © The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org 1653