Enabling dynamic network analysis through visualization in TVNViewer

Abstract

BackgroundMany biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.ResultsIn this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.ConclusionsTVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

A Kernel Multiple Change-point Algorithm via Model Selection

“…Example 5 The set X is a set of graphs. For instance, the X i can represent a social network (Kossinets and Watts, 2006) or a biological network (Curtis et al, 2012) that is changing over time (Chen and Zhang, 2015). Then, detecting meaningful changes in the structure of a time-varying network is a change-point problem.…”

“…When the number of change-points is known, this problem reduces to estimating the change-point locations as precisely as possible; in general, the number of change-points itself must be estimated. This problem arises in a wide range of applications, such as bioinformatics (Picard et al, 2005;Curtis et al, 2012), neuroscience (Park et al, 2015), audio signal processing (Wu and Hsieh, 2006), temporal video segmentation (Koprinska and Carrato, 2001), hacker-attacks detection (Wang et al, 2014), social sciences (Kossinets and Watts, 2006) and econometrics (McCulloh, 2009).…”

High-dimensional Change-point Detection Using Generalized Homogeneity Metrics

“…Change-point detection has been a classical and well-established problem in statistics and econometrics, aiming to detect the lack of homogeneity in a sequence of time-ordered observations. It finds abundance of applications in a wide variety of fields, for example, bioinformatics (Picard et al, 2005;Curtis et al, 2012), neuroscience (Park et al, 2015), digital speech processing (Rabiner and Schäfer, 2007), social network analysis (McCulloh, 2009), and so on. We refer the reader to Aue and Horváth (2013) and Jandhyala et al (2013) for some recent reviews on this topic.…”