DTW-MIC Coexpression Networks from Time-Course Data [article]

Samantha Riccadonna and Giuseppe Jurman and Roberto Visintainer and Michele Filosi and Cesare Furlanello
2014 arXiv   pre-print
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
more » ... IC) 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.
arXiv:1210.3149v2 fatcat:m2hurjqkqrgmzfirqqlh6lvf7a