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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Characterization of Subgroup Patterns from Graphical Representation of Genomic Data
verfasst von : Sangkyun Lee
Erschienen in: Brain Informatics and Health
Verlag: Springer International Publishing
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High-throughput genomic profiling technology provides us detailed information of biological systems. However, it also increases the dimensionality in data, which makes it harder to identify key features and their relations to other features hidden in feature spaces. In this paper we propose a new idea based on the structure learning for the Gaussian Markov random field, which provides us an efficient way to represent a feature space as a collection of small graphs, where nodes represent features and edges represent conditional dependency between features. In our approach a collection of small graphs is created for each subgroup of a cohort, where our interest lies in finding characteristic patterns in each subgroup graph compared to the other subgroup graphs. A simple but effective method is proposed using polarized adjacency matrices to find topological differences in collections of graphs.