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Module Detection in Dynamic Networks by Temporal Edge Weight Clustering

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

While computational systems biology provides a rich array of methods for network clustering, most of them are not suitable to capture cellular network dynamics. In the most common setting, computational algorithms seek to integrate the static information embedded in near-global interaction networks with the temporal information provided by time series experiments. We present a novel technique for temporally informed network module detection, named TD-WGcluster (Time Delay Weighted Graph CLUSTERing). TD-WGcluster utilizes four steps: (i) time-lagged correlations are calculated between any couple of interacting nodes in the network; (ii) an unsupervised version of k-means algorithm detects sub-graphs with similar time-lagged correlation; (iii) a fast-greedy optimization algorithm identify connected components by sub-graph; (iv) a geometric entropy is computed for each connected component as a measure of its complexity. TD-WGcluster notable feature is the attempt to account for temporal delays in the formation of regulatory modules during signal propagation in a network.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Trento, Via Sommarive 14, 38123, Povo, Trento, Italy

    Paola Lecca

  2. Association for Computing Machinery, New York, USA

    Paola Lecca

  3. Laboratory of Translational Genomics, Centre for Integrative Biology, Univesrity of Trento, Via Sommarive 9, 38123, Povo, Trento, Italy

    Angela Re

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  1. Paola Lecca
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  2. Angela Re
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Correspondence to Paola Lecca .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

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© 2016 Springer International Publishing Switzerland

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Lecca, P., Re, A. (2016). Module Detection in Dynamic Networks by Temporal Edge Weight Clustering. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-44332-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44331-7

  • Online ISBN: 978-3-319-44332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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