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Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50127

Costa,  Ivan G.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50394

Krause,  Roland
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

Schliep,  Alexander
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Fulltext (public)

1471-2105-8-S10-S3.pdf
(Any fulltext), 3MB

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Citation

Costa, I. G., Krause, R., Opitz, L., & Schliep, A. (2007). Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data. Whistler, Canada: BioMed Central Ltd.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-80F5-5
Abstract
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. Complimentary large data sets of in situ RNA hybridization images for different stages of the fly embryo elucidate the spatial expression patterns. Results: Using a semi-supervised approach, constrained clustering with mixture models, we can find clusters of genes exhibiting spatio-temporal similarities in expression, or syn-expression. The temporal gene expression measurements are taken as primary data for which pairwise constraints are computed in an automated fashion from raw in situ images without the need for manual annotation. We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results. Conclusion: Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data. Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance.