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Interactive visualization of large-scale gene expression data
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Interaction Lab)ORCID iD: 0000-0001-6310-346X
Takara Bio Europe, Gothenburg, Sweden.
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. Astra Zeneca, Mölndal, Sweden. (Bioinformatik, Bioinformatics)
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2016 (English)In: Information Visualisation: Computer Graphics, Imaging and Visualisation / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Remo Burkhard, John Counsell, Urska Cvek, Martin J. Eppler, Georges Grinstein, Wei Dong Huang, Sebastian Kernbach, Chun-Cheng Lin, Feng Lin, Francis T. Marchese, Chi Man Pun, Muhammad Sarfraz, Marjan Trutschl, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, and Jian J. Zhang, IEEE Computer Society, 2016, 348-354 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we present an interactive prototype that aids the interpretation of large-scale gene expression data, showing how visualization techniques can be applied to support knowledge extraction from large datasets. The developed prototype was evaluated on a dataset of human embryonic stem cell-derived cardiomyocytes. The visualization approach presented here supports the analyst in finding genes with high similarity or dissimilarity across different experimental groups. By using an external overview in combination with filter windows, and various color scales for showing the degree of similarity, our interactive visual prototype is able to intuitively guide the exploration processes over the large amount of gene expression data.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. 348-354 p.
Series
Proceedings [IEEE], E-ISSN 2375-0138
Keyword [en]
decision-making, gene expression data, similarity, visual analytics
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-12959DOI: 10.1109/IV.2016.58ISI: 000389494200057Scopus ID: 2-s2.0-84989862491ISBN: 978-1-4673-8942-6 (electronic)ISBN: 978-1-4673-8943-3 (print)OAI: oai:DiVA.org:his-12959DiVA: diva2:974128
Conference
20th International Conference Information Visualisation, 19-22 July 2016, Lisbon, Portugal
Projects
NOVA and BISON
Funder
Knowledge Foundation, 20140294
Available from: 2016-09-24 Created: 2016-09-24 Last updated: 2017-11-27Bibliographically approved

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Riveiro, MariaLebram, MikaelSartipy, PeterSynnergren, Jane

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