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Athanasios Vogogias, Jessie Kennedy, Daniel Archambault, Benjamin Bach, V. Anne Smith, Hannah Currant
2018 ACM Transactions on Intelligent Systems and Technology  
We address the problem of exploring, combining and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In this eld, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical t to the data. e goal of the analyst is to guide the heuristic search and decide how to determine a nal consensus network structure, usually by
more » ... cting the top-scoring network or constructing the consensus network from a collection of high-scoring networks. BayesPiles, our visualisation tool, helps with understanding the structure of the solution space and supporting the construction of a nal consensus network that is representative of the underlying dataset. BayesPiles builds upon and extends MultiPiles to meet our domain requirements. We developed BayesPiles in conjunction with computational biologists who have used this tool on datasets used in their research. e biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.
doi:10.1145/3230623 fatcat:vwu2n53jtze5fiyk7a4pkgcqwe

Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data [article]

Athanasios Vogogias, Jessie Kennedy, Daniel Archambault
2016 EuroVis 2016 - Posters  
on branches at the top view, highlights time-series at the linked bottom view. 1 School of Computing, Edinburgh Napier University 2 Department of Computer Science, Swansea University Thanasis Vogogias  ... 
doi:10.2312/eurp.20161127 dblp:conf/vissym/VogogiasKA16 fatcat:bajtlzzglnhavcqgy5b3rzz2eu

MLCut: Exploring Multi-Level Cuts in Dendrograms for Biological Data [article]

Athanasios Vogogias, Jessie Kennedy, Daniel Archambault, V. Anne Smith, Hannah Currant
2016 Computer Graphics and Visual Computing (CGVC)  
A draft of this prototype was presented in Vogogias et al. Coordinated views During HC, information is extracted from the original data.  ... 
doi:10.2312/cgvc.20161288 dblp:conf/tpcg/VogogiasKASC16 fatcat:abhkq53dnjfdnnigfrdattly7a

Visual Encodings for Networks with Multiple Edge Types

Athanasios Vogogias, Daniel Archambault, Benjamin Bach, Jessie Kennedy
2020 Proceedings of the International Conference on Advanced Visual Interfaces  
Figure 1 : Examples of visual designs considered for encoding multiple types of edges in matrices. The top row shows an example of a single matrix and the bottom row shows the encoding for each edge type. The encodings use one or more visual variables to represent multiple edges: a) uses a coloured pie chart, b) uses opacity in a pie chart, c) uses a segmented and coloured pie chart d) uses orientation, e) combines position and colour, f) uses size and g) combines size and colour to create a
more » ... ph. The designs (d) and (e) were used in our experiments. ABSTRACT 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, selecting four designs from a potentially huge design space of visual encodings. We then settle on three visual variables to evaluate in a crowdsourcing study with 159 participants: orientation, position and colour. The best encodings were integrated into a visual analytics tool for inferring dynamic Bayesian networks and evaluated by computational biologists for additional evidence. We found that the encodings performed differently depending on the task, however, colour was found to help in all tasks except when trying to find the edge with the largest number of edge types. Orientation generally outperformed position in all of our tasks.
doi:10.1145/3399715.3399827 dblp:conf/avi/VogogiasABK20 fatcat:k4iguksr6rfuxnjym2i7vfq2au