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BayesPiles: Visualisation Support for Bayesian Network Structure Learning

Athanasios Vogogias, Jessie Kennedy, Daniel Archambault Orcid Logo, 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 Orcid Logo

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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa39457
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spelling 2020-07-13T13:22:58.7662029 v2 39457 2018-04-17 BayesPiles: Visualisation Support for Bayesian Network Structure Learning 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2018-04-17 SCS 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. Journal Article ACM Transactions on Intelligent Systems and Technology 10 1 1 23 21576904 31 12 2018 2018-12-31 10.1145/3230623 https://dlnext.acm.org/doi/abs/10.1145/3230623 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-07-13T13:22:58.7662029 2018-04-17T13:51:20.7364865 Athanasios Vogogias 1 Jessie Kennedy 2 Daniel Archambault 0000-0003-4978-8479 3 Benjamin Bach 4 V. Anne Smith 5 Hannah Currant 6 0039457-17042018140110.pdf bayespiles-visualisation-support.pdf 2018-04-17T14:01:10.4700000 Output 3445914 application/pdf Accepted Manuscript true 2018-11-30T00:00:00.0000000 "© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Intelligent Systems and Technology, {VOL 10, No 1, Article 5 (November 2018)} https://doi.org/10.1145/3230623" true eng
title BayesPiles: Visualisation Support for Bayesian Network Structure Learning
spellingShingle BayesPiles: Visualisation Support for Bayesian Network Structure Learning
Daniel Archambault
title_short BayesPiles: Visualisation Support for Bayesian Network Structure Learning
title_full BayesPiles: Visualisation Support for Bayesian Network Structure Learning
title_fullStr BayesPiles: Visualisation Support for Bayesian Network Structure Learning
title_full_unstemmed BayesPiles: Visualisation Support for Bayesian Network Structure Learning
title_sort BayesPiles: Visualisation Support for Bayesian Network Structure Learning
author_id_str_mv 8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv 8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Daniel Archambault
author2 Athanasios Vogogias
Jessie Kennedy
Daniel Archambault
Benjamin Bach
V. Anne Smith
Hannah Currant
format Journal article
container_title ACM Transactions on Intelligent Systems and Technology
container_volume 10
container_issue 1
container_start_page 1
publishDate 2018
institution Swansea University
issn 21576904
doi_str_mv 10.1145/3230623
url https://dlnext.acm.org/doi/abs/10.1145/3230623
document_store_str 1
active_str 0
description 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.
published_date 2018-12-31T03:50:06Z
_version_ 1763752444295118848
score 11.036116

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