1,721,090 research outputs found
Identification of Tmem10/Opalin as a novel marker for oligodendrocytes using gene expression profiling
Full text linkAbstract Background During the development of the central nervous system, oligodendrocytes generate large amounts of myelin, a multilayered insulating membrane that ensheathes axons, thereby allowing the fast conduction of the action potential and maintaining axonal integrity. Differentiation of oligodendrocytes to myelin-forming cells requires the downregulation of RhoA GTPase activity. Results To gain insights into the molecular mechanisms of oligodendrocyte differentiation, we performed microarray expression profiling of the oligodendroglial cell line, Oli-neu, treated with the Rho kinase (ROCK) inhibitor, Y-27632 or with conditioned neuronal medium. This resulted in the identification of the transmembrane protein 10 (Tmem10/Opalin), a novel type I transmembrane protein enriched in differentiating oligodendrocytes. In primary cultures, Tmem10 was abundantly expressed in O4-positive oligodendrocytes, but not in oligodendroglial precursor cells, astrocytes, microglia or neurons. In mature oligodendrocytes Tmem10 was enriched in the rims and processes of the cells and was only found to a lesser extent in the membrane sheets. Conclusion Together, our results demonstrate that Tmem10 is a novel marker for in vitro generated oligodendrocytes.</p
Major histocompatibility complex (MHC) gene expression profiling of the graft versus host reaction in skin explant assays
No full textMajor histocompatibility complex (MHC) gene expression profiling of the graft versus host reaction in skin explant assays
No full textGene expression profiling of brains from bovine spongiform encephalopathy (BSE)-infected cynomolgus macaques
No full textGene expression profiling of brains from bovine spongiform encephalopathy (BSE)-infected cynomolgus macaques
No full textMolecular genomics of cardiac remodeling following changes in intracellular calcium release
No full textMolecular genomics of cardiac remodeling following changes in intracellular calcium release
No full textGlobal analysis of asymmetric RNA enrichment in oocytes reveals low conservation between closely related Xenopus species
No full textISSN:1939-4586ISSN:1059-1524ISSN:1059-152
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.
Full text linkBackground:
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
A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data
No full textPre-therapeutic prediction of therapy response and clinical outcome is an important field in medicine and poses new challenges to statisticians. Statistical prediction rules for two-class problems are generally designated to maximize the overall correct classification rate and to reflect an optimal balance of sensitivity and specificity. In some clinical situations, however, correct prediction of one particular class is more important than of the other class. We therefore propose a new strategy of building prediction rules, which are designed to increase the sensitivity, while losing some specificity. This strategy is applied to artificial simulation data and to gene expression data from primary colon cancers. Our concept is generally applicable to most common classification methods. (C) 2010 Elsevier Ireland Ltd. All rights reserved
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