  When modeling coexpression networks from high-throughput time course data,
Pearson Correlation Coefficient (PCC) is one of the most effective and popular
similarity functions. However, its reliability is limited since it cannot
capture non-linear interactions and time shifts. Here we propose to overcome
these two issues by employing a novel similarity function, Dynamic Time Warping
Maximal Information Coefficient (DTW-MIC), combining a measure taking care of
functional interactions of signals (MIC) and a measure identifying horizontal
displacements (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to
quantify network differences, the effectiveness of the DTW-MIC approach is
demonstrated on both synthetic and transcriptomic datasets.
