Skip to content

Research at St Andrews

BayesPiles: visualisation support for Bayesian network structure learning

Research output: Contribution to journalArticle

DOI

Open Access permissions

Open

Author(s)

Athanasios Vogogias, Jessie Kennedy, Daniel Archambault, Benjamin Bach, V Anne Smith, Hannah Currant

School/Research organisations

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.
Close

Details

Original languageEnglish
Article number5
Number of pages23
JournalACM Transactions on Intelligent Systems and Technology
Volume10
Issue number1
Early online date28 Nov 2018
DOIs
Publication statusPublished - Nov 2018

    Research areas

  • Visualisation, Graphs, Bioinformatics

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. MLCut: exploring Multi-Level Cuts in dendrograms for biological data

    Vogogias, A., Kennedy, J., Archaumbault, D., Smith, V. A. & Currant, H., 16 Sep 2016, Computer Graphics and Visual Computing Conference (CGVC) 2016. Turkay, C. & Wan, T. R. (eds.). Eurographics Association

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Dynamic modulation of phosphoprotein expression in ovarian cancer xenograft models

    Koussounadis, A., Langdon, S., Um, I. H., Kay, C., Francis, K., Harrison, D. J. & Smith, V. A., 10 Mar 2016, In : BMC Cancer. 16, 13 p., 205.

    Research output: Contribution to journalArticle

  3. Biological network inference at multiple scales: from gene regulation to species interactions

    Aderhold, A., Smith, V. A. & Husmeier, D., 2016, Pattern Recognition in Computational Molecular Biology: Techniques and Approaches. Elloumi, M., Iliopoulos, C. S., Wang, J. T. L. & Zomaya, A. Y. (eds.). Wiley-Blackwell, p. 525-554 (Wiley Book Series on Bioinformatics: Computational Techniques and Engineering).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  4. Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis

    Verleyen, W., Langdon, S. P., Faratian, D., Harrison, D. J. & Smith, V. A., 27 Oct 2015, In : Scientific Reports. 5, 7 p., 15563.

    Research output: Contribution to journalArticle

  5. Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system

    Koussounadis, A., Langdon, S., Um, I. H., Harrison, D. J. & Smith, V. A., 8 Jun 2015, In : Scientific Reports. 5, 9 p., 10775.

    Research output: Contribution to journalArticle

ID: 252830854