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Bioinformatics Advance Access originally published online on May 10, 2005
Bioinformatics 2005 21(14):3146-3154; doi:10.1093/bioinformatics/bti484
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Knowledge-based computational search for genes associated with the metabolic syndrome

Tsutomu Matsunaga 1,* and Masa-aki Muramatsu 2,3

1Research and Development Headquarters, NTT DATA Corporation Tokyo, 104-0033 Japan
2Medical Research Institute, Tokyo Medical and Dental University Tokyo, 113-8510 Japan
3Research Institute, HuBit Genomix Inc. Tokyo, 102-0092 Japan

*To whom correspondence should be addressed.

Motivation: A methodology to search for genes associated with multifactorial diseases by integrating the large amount of accumulated knowledge is seriously needed. A comprehensive understanding derived from a holistic view of gene relationship structures can be gained from our proposed analysis called the cross-subspace analysis (CSA). In this analysis, gene objects are generated by machine learning using their term occurrence patterns in MEDLINE abstracts and the degree of relationship between gene objects is quantified by matching these patterns.

Results: Structuralization of relationships of a set of genes was performed using CSA, which were retrieved using the terms, ‘obesity’, ‘diabetes’, ‘hypertriglyceridemia’ and ‘hypertension’ that refer to diseases comprising metabolic syndrome, on a 2D plane inferring important biomedical concepts from the gene distribution. Then, we prioritized the significance of 6131 well-annotated human genes in terms of the distance on the plane from the centroid of ‘metabolic syndrome’-related genes distribution. The validity was confirmed by comparing the knowledge extracted by the ordering with existing medical knowledge.

Contact: matsunagat{at}nttdata.co.jp

Received on January 18, 2005; revised on April 24, 2005; accepted on May 2, 2005

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