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1.  A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text 
Bioinformatics  2013;29(13):i44-i52.
Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge.
Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches.
Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText.
Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/.
Contact: makoto.miwa@manchester.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt227
PMCID: PMC3694679  PMID: 23813008
2.  Event extraction across multiple levels of biological organization 
Bioinformatics  2012;28(18):i575-i581.
Motivation: Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability.
Results: We extend the event extraction approach to biomedical information extraction to encompass all levels of biological organization from the molecular to the whole organism. We present the ontological foundations, target types and guidelines for entity and event annotation and introduce the new multi-level event extraction (MLEE) corpus, manually annotated using a structured representation for event extraction. We further adapt and evaluate named entity and event extraction methods for the new task, demonstrating that both can be achieved with performance broadly comparable with that for established molecular entity and event extraction tasks.
Availability: The resources and methods introduced in this study are available from http://nactem.ac.uk/MLEE/.
Contact: pyysalos@cs.man.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts407
PMCID: PMC3436834  PMID: 22962484

Results 1-2 (2)