Discovering and visualizing indirect associations between biomedical concepts

Yoshimasa Tsuruoka, Makoto Miwa, Kaisei Hamamoto, Jun'ichi Tsujii, Sophia Ananiadou

    Research output: Contribution to journalArticlepeer-review


    Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner. Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. © The Author(s) 2011. Published by Oxford University Press.
    Original languageEnglish
    Article numberbtr214
    Pages (from-to)111-119
    Number of pages8
    Issue number13
    Publication statusPublished - Jul 2011


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