  A computational framework utilizes the traditional similarity measures for
mining the significant relationships in biological annotations is recently
proposed by Tatiana V. Karpinets et al. [2]. In this paper, an improved
approximation algorithm for MIC (maximal information coefficient) named IAMIC
is suggested to perfect this framework for discovering the hidden regularities
between biological annotations. Further, IAMIC is the enhanced algorithm for
approximating a novel similarity coefficient MIC with generality and
equitability, which makes it more appropriate for data exploration. Here it is
shown that IAMIC is also applicable for identify the associations between
biological annotations.
