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          Institute: MPI für molekulare Genetik     Collection: Department of Computational Molecular Biology     Display Documents



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ID: 335839.0, MPI für molekulare Genetik / Department of Computational Molecular Biology
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.
Authors:Costa, Ivan G.; Krause, Roland; Opitz, Lennard; Schliep, Alexander
Language:English
Place of Publication:Whistler, Canada
Publisher:BioMed Central Ltd.
Date of Publication (YYYY-MM-DD):2007-12-21
Title of Journal:BMC Bioinformatics
Journal Abbrev.:BMC Bioinformatics
Issue / Number:8(Suppl 10)
Title of Issue:Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology
Name of Conference/Meeting:Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology
Place of Conference/Meeting:Whistler, Canada
(Start) Date of Conference 
(YYYY-MM-DD):
2006-12-08
End Date of Conference
(YYYY-MM-DD):
2006-12-08
Copyright:© 2007 Costa et al; licensee BioMed Central Ltd.
Audience:Experts Only
Abstract / Description: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.
Comment of the Author/Creator:Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
email: costa@molgen.mpg.de
email:schliep@molgen.mpg.de
External Publication Status:published
Document Type:Proceedings
Communicated by:Martin Vingron
Affiliations:MPI für molekulare Genetik
MPI für Infektionsbiologie/Department of Cellular Microbiology
External Affiliations:1.Abteilung Entwicklungsbiochemie, Universität Göttingen, Göttingen, Germany.
Identifiers:ISSN:1471-2105
DOI:10.1186/1471-2105-8-S10-S3
URL:http://www.biomedcentral.com/content/pdf/1471-2105...
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