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All Outputs (3)

Visual Encodings for Networks with Multiple Edge Types (2020)
Conference Proceeding
Vogogias, T., Archambault, D. W., Bach, B., & Kennedy, J. (2020). Visual Encodings for Networks with Multiple Edge Types. In AVI '20: Proceedings of the International Conference on Advanced Visual Interfaces. https://doi.org/10.1145/3399715.3399827

This paper reports on a formal user study on visual encodings of networks with multiple edge types in adjacency matrices. Our tasks and conditions were inspired by real problems in computational biology. We focus on encodings in adjacency matrices, s... Read More about Visual Encodings for Networks with Multiple Edge Types.

MLCut: exploring multi-level cuts in dendrograms for biological data (2016)
Conference Proceeding
Vogogias, A., Kennedy, J., Archambault, D., Anne Smith, V., & Currant, H. (2016). MLCut: exploring multi-level cuts in dendrograms for biological data. In C. Turkay, & T. Ruan Wan (Eds.), Computer Graphics and Visual Computing (CGVC). https://doi.org/10.2312/cgvc.20161288

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple... Read More about MLCut: exploring multi-level cuts in dendrograms for biological data.

Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data (2016)
Conference Proceeding
Vogogias, A., Kennedy, J., & Archambault, D. (2016). Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data. In T. Isenberg, & F. Sadlo (Eds.), EuroVis 2016 - Posters (1-3). https://doi.org/10.2312/eurp.20161127

Rigid adherence to pre-specified thresholds and static graphical representations can lead to incorrect decisions on merging of clusters. As an alternative to existing automated or semi-automated methods, we developed a visual analytics approach for p... Read More about Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data.