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BayesPiles: Visualisation Support for Bayesian Network Structure Learning / Athanasios Vogogias; Jessie Kennedy; Daniel Archambault; Benjamin Bach; V. Anne Smith; Hannah Currant

ACM Transactions on Intelligent Systems and Technology, Volume: 10, Issue: 1, Pages: 1 - 23

Swansea University Author: Daniel, Archambault

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DOI (Published version): 10.1145/3230623

Abstract

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 field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parame...

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Published in: ACM Transactions on Intelligent Systems and Technology
ISSN: 21576904
Published: 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa39457
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Abstract: 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 field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical fit to the data. The goal of the analyst is to guide the heuristic search and decide how to determine a final consensus network structure, usually by selecting 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 final 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. The biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.
Issue: 1
Start Page: 1
End Page: 23