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Discovering and visualizing indirect associations between biomedical concepts.

Tsuruoka Y, Miwa M, Hamamoto K, Tsujii J, Ananiadou S - Bioinformatics (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp

ABSTRACT

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.

Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/.

Contact: tsuruoka@jaist.ac.jp.

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Related in: MedlinePlus

Finding indirectly associated concepts.
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Figure 1: Finding indirectly associated concepts.

Mentions: A common approach to automatic discovery of useful hypotheses is to combine two (or more) known associations, i.e. if concept X is associated with concept Y, and concept Y is associated with concept Z, then the potential association between X and Z is considered as a useful hypothesis unless there is already a known association between X and Z. This approach is often called Swanson's ABC model approach after his seminal work on literature-based hypothesis generation (Swanson, 1990). Figure 1 illustrates this approach in the context of implementing it on FACTA+, where the user provides a starting concept as a query to the system. We call the concepts that are directly associated with the query the pivot concepts, and the concepts that are indirectly associated with the query through those pivot concepts the target concepts.Fig. 1.


Discovering and visualizing indirect associations between biomedical concepts.

Tsuruoka Y, Miwa M, Hamamoto K, Tsujii J, Ananiadou S - Bioinformatics (2011)

Finding indirectly associated concepts.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117364&req=5

Figure 1: Finding indirectly associated concepts.
Mentions: A common approach to automatic discovery of useful hypotheses is to combine two (or more) known associations, i.e. if concept X is associated with concept Y, and concept Y is associated with concept Z, then the potential association between X and Z is considered as a useful hypothesis unless there is already a known association between X and Z. This approach is often called Swanson's ABC model approach after his seminal work on literature-based hypothesis generation (Swanson, 1990). Figure 1 illustrates this approach in the context of implementing it on FACTA+, where the user provides a starting concept as a query to the system. We call the concepts that are directly associated with the query the pivot concepts, and the concepts that are indirectly associated with the query through those pivot concepts the target concepts.Fig. 1.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp

ABSTRACT

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.

Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/.

Contact: tsuruoka@jaist.ac.jp.

Show MeSH
Related in: MedlinePlus