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Interactive visualization of large-scale gene expression data
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-2900-9335
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Interaction Lab)ORCID-id: 0000-0001-6310-346X
Takara Bio Europe, Gothenburg, Sweden.
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. Astra Zeneca, Mölndal, Sweden. (Bioinformatik, Bioinformatics)
Vise andre og tillknytning
2016 (engelsk)Inngår i: 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, s. 348-354Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2016. s. 348-354
Serie
Proceedings [IEEE], E-ISSN 2375-0138
Emneord [en]
decision-making, gene expression data, similarity, visual analytics
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB); Bioinformatik
Identifikatorer
URN: urn:nbn:se:his:diva-12959DOI: 10.1109/IV.2016.58ISI: 000389494200057Scopus ID: 2-s2.0-84989862491ISBN: 978-1-4673-8942-6 (digital)ISBN: 978-1-4673-8943-3 (tryckt)OAI: oai:DiVA.org:his-12959DiVA, id: diva2:974128
Konferanse
20th International Conference Information Visualisation, 19-22 July 2016, Lisbon, Portugal
Prosjekter
NOVA and BISON
Forskningsfinansiär
Knowledge Foundation, 20140294Tilgjengelig fra: 2016-09-24 Laget: 2016-09-24 Sist oppdatert: 2018-03-28bibliografisk kontrollert

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