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/.
Previous item | Next item |
Back to news summary page |
Featured News
- New Named Entity Corpus for Occupational Substance Exposure Assessment
- FinNLP-FNP-LLMFinLegal @ COLING-2025 - Call for papers
- Keynote talk at Manchester Law and Technology Conference
- Keynote talk at ACM Summer School on Data Science, Athens
- Congratulations to PhD student Panagiotis Georgiades
Other News & Events
- Invited talk at the 8th Annual Women in Data Science Event at the American University of Beirut
- Invited talk at the 2nd Symposium on NLP for Social Good (NSG), University of Liverpool
- Invited talk at Annual Meeting of the Danish Society of Occupational and Environmental Medicine
- Advances in Data Science and Artificial Intelligence Conference 2024
- New review article on emotion detection for misinformation