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

Abstract

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. 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.