A conceptual approach to gene expression analysis enhanced by visual analytics

Authors:
Cassio Melo;Marie-Aude Aufaure;Constantinos Orphanides;Simon Andrews;Kenneth McLeod;Albert Burger
Affiliations:
École Centrale Paris, Châtenay-Malabry, France;École Centrale Paris, Châtenay-Malabry, France;Sheffield Hallam University, Sheffield, United Kingdom;Sheffield Hallam University, Sheffield, United Kingdom;Heriot-Watt University, Edinburgh, Scotland;Heriot-Watt University, Edinburgh, Scotland
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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Abstract

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.