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Bioinformatics Advance Access published online on May 10, 2005
Bioinformatics, 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@oupjournals.org
Received January 18, 2005
Revised April 24, 2005
Accepted May 2, 2005

Article

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

Tsutomu Matsunaga 1* and Masa-aki Muramatsu 2

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

* To whom correspondence should be addressed.
Tsutomu Matsunaga, E-mail: matsunagat{at}nttdata.co.jp


  Abstract

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 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: CSA performed structuralization of relationships of a set of genes, which are retrieved using the terms, "obesity", "diabetes", "hypertriglyceridemia" and "hypertension" that refer to diseases comprising metabolic syndrome, on a two-dimensional plane inferring important biomedical concepts from the gene distribution. Then, we prioritized the significance of 6,131 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.


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