Home Aims & Objectives NaCTeM Services Text Mining Tools Text Mining Infrastructures Text Mining for Biodiversity Resources Terms & Conditions FAQ News & Events News What others are saying about us Press and Journal Mentions NaCTeM Seminars People Projects Publications Community External Collaboration Vacancies Teaching & Tutorials Feedback How to find us

New paper on integrating and ranking textual evidence for biochemical pathways

2013-06-27

We are pleased to announce the publication of a new journal article that presents a 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. The methods have been used to update our existing pathway search system, PathText.

Miwa, M., Ohta, T., Rak, R., Rowley, A., Kell, D. B., Pyysalo, S. and Ananiadou, S. (2013). A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text. Bioinformatics, 29(13), i44-i52 and Proceedings of ISMB 2013.

Full abstract

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/.