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      DTW-MIC Coexpression Networks from Time-Course Data

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          Abstract

          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.

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          Author and article information

          Journal
          11 October 2012
          2014-10-16
          Article
          1210.3149
          91334549-4956-43d9-ba0c-4e5f79d98a77

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          q-bio.MN

          Molecular biology
          Molecular biology

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