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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

Visualization of directly associated concepts using treemapping.
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Figure 3: Visualization of directly associated concepts using treemapping.

Mentions: E-cadherin is a cell adhesion molecule involved with the binding between a cell and other cells or extracellular matrix. The search results shown in Figures 2 and 4 indicate that E-cadherin is associated with multiple nervous system disorders (e.g. Alzheimer's disease, Parkinson's disease, epilepsy) via several proteins/genes, even though E-cadherin itself rarely appears with such disorders (see also Fig. 3 for direct associations). This indirect search result suggests that E-cadherin could be a potential candidate of drug target for nervous system disorders.Fig. 3.


Discovering and visualizing indirect associations between biomedical concepts.

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

Visualization of directly associated concepts using treemapping.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: Visualization of directly associated concepts using treemapping.
Mentions: E-cadherin is a cell adhesion molecule involved with the binding between a cell and other cells or extracellular matrix. The search results shown in Figures 2 and 4 indicate that E-cadherin is associated with multiple nervous system disorders (e.g. Alzheimer's disease, Parkinson's disease, epilepsy) via several proteins/genes, even though E-cadherin itself rarely appears with such disorders (see also Fig. 3 for direct associations). This indirect search result suggests that E-cadherin could be a potential candidate of drug target for nervous system disorders.Fig. 3.

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