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.