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BayesPiles : visualisation support for Bayesian network structure learning

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Vogogias_2018_BayesPiles_ACMTIST_AAM.pdf (3.286Mb)
Date
11/2018
Author
Vogogias, Athanasios
Kennedy, Jessie
Archambault, Daniel
Bach, Benjamin
Smith, V Anne
Currant, Hannah
Keywords
Visualisation
Graphs
Bioinformatics
QA75 Electronic computers. Computer science
QA76 Computer software
QH301 Biology
NDAS
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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.
Citation
Vogogias , A , Kennedy , J , Archambault , D , Bach , B , Smith , V A & Currant , H 2018 , ' BayesPiles : visualisation support for Bayesian network structure learning ' , ACM Transactions on Intelligent Systems and Technology , vol. 10 , no. 1 , 5 . https://doi.org/10.1145/3230623
Publication
ACM Transactions on Intelligent Systems and Technology
Status
Peer reviewed
DOI
https://doi.org/10.1145/3230623
ISSN
2157-6904
Type
Journal article
Rights
© 2018, Association for Computing Machinery. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3230623
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  • Biology Research
  • Centre for Biological Diversity (CBD) Research
  • Institute of Behavioural and Neural Sciences Research
  • Scottish Oceans Institute Research
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/16636

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