Our joint study with Dr Caban has been accepted for publication in IEEE EMBC 2012!
In this study, we explored non-linear dependencies among pairs of clinical and imaging variables pertaining to patients with lung disorders, fibrosis in particular. Our study extends the work published in SCIENCE magazine (by Reshef et al) and uses maximal information coefficient (MIC) metric and its unique properties to find novel associations among weakly correlating data. My recent comment on Reshef’s marvelous paper can be found from HERE!
Maximum Asymmetry Score (MAS), Maximum Edge Value (MEV), Minimum Cell Number (MCN), as well as MIC were analyzed in detail while constructing meaningful relationship among weakly correlating data.
Once the final version of the paper is uploaded, it will be available from this website.
Current citation information of the paper is the following:
J.Caban, U.Bagci, D.J. Mollura, Characterizing Non-Linear Dependencies Among Pairs of Clinical Variables and Imaging Data. IEEE EMBC 2012.