Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
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DOI：
10.1186/1471-2105-8-s10-s3
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发表时间：
2007-01-01
影响因子：
3
通讯作者：
Schliep, Alexander
中科院分区：
文献类型：
--
作者：
Costa, Ivan G.;Krause, Roland;Schliep, Alexander
背景资料：果蝇发育过程中的基因表达测量通常用于寻找时间共表达基因的功能模块。免费的大数据集的原位RNA杂交图像的不同阶段的苍蝇embryon.Results阐明的空间表达patterns：使用半监督的方法，约束聚类与混合模型，我们可以找到集群的基因表现出时空相似性的表达，或同步表达。时间基因表达测量值被作为主要数据，其中成对约束以自动方式从原始原位图像计算，而不需要手动注释。我们调查的影响，这些成对的约束条件的聚类和讨论的生物相关性，我们的results.Conclusion：空间信息有助于详细的，生物有意义的分析时间基因表达数据。半监督学习提供了一个灵活，强大和有效的框架，用于集成不同质量和丰富度的数据源。
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.