  Discovering associations is of central importance in scientific practices.
Currently, most researches consider only linear association measured by
correlation coefficient, which has its theoretical limitations. In this paper,
we propose a new method for discovering association with copula entropy -- a
universal applicable association measure for not only linear cases, but
nonlinear cases. The advantage of the method based on copula entropy over
traditional method is demonstrated on the NHANES data by discovering more
biomedical meaningful associations.
