| Autor: |
Zheng M; Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA., Domanskyi S; Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA., Piermarocchi C; Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA., Mias GI; Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA. gmias@msu.edu.; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA. gmias@msu.edu.; Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA. gmias@msu.edu. |
| Abstrakt: |
Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications. |
