BayesPiles

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