HyperPrior is a hypergraph-based semi-supervised learning algorithm to classify gene expression and arrayCGH data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.
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Bioinformatics. 2009 Nov 1;25(21):2831-8. doi: 10.1093/bioinformatics/btp467. Epub 2009 Jul 30.
A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge.
Tian Z, Hwang T, Kuang R.