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This article is part of the supplement: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology

Open Access Open Badges Proceedings

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

Ivan G Costa1*, Roland Krause13, Lennart Opitz2 and Alexander Schliep1*

Author Affiliations

1 Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany

2 Abteilung Entwicklungsbiochemie, Universität Göttingen, Göttingen, Germany

3 Department Cellular Microbiology, Max Planck Institute for Infection Biology, Berlin, Germany

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BMC Bioinformatics 2007, 8(Suppl 10):S3  doi:10.1186/1471-2105-8-S10-S3

Published: 21 December 2007



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.


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.


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.