Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields.
Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields.
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DOI：
10.1093/bioinformatics/btt206
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发表时间：
2013-07-01
期刊：
影响因子：
--
通讯作者：
Ohler U
中科院分区：
文献类型：
--
作者：
Pruteanu-Malinici I;Majoros WH;Ohler U
动机：图像数据中表型注释的计算方法显示了许多应用程序的有希望的结果，并为研究基因功能和相互作用提供了丰富而有价值的信息。虽然通常在高空间分辨率和多个时间点上都可以使用数据，但仅对于单个时间点，表型经常被独立注释。特别是，对于开发基因表达模式的分析，当共同考虑跨多个时间点的图像时，它在生物学上是明智的，因此同时捕获了空间和时间依赖性。
方法：我们描述了一种基于连接树算法的有效训练和解码方法，描述了一种标记基因表达时间序列数据的歧视性无向图形模型。该方法基于一种有效的特征选择技术，该技术由非参数稀疏的贝叶斯因子分析模型组成。结果是一个灵活的框架，它可以处理具有嘈杂的样本的大规模数据，即它可以忍受各个时间点缺少的数据。
结果：使用跨果蝇胚胎发育阶段的基因表达模式的注释为例，我们证明我们的方法可以实现卓越的准确性，与以前的模型相比，通过共同注释表型序列获得了卓越的准确性。缺少数据的实验结果表明，我们的联合学习方法成功注释了一个或多个阶段无表达数据的基因。
联系人：uwe.ohler@duke.edu
Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously. Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points. Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages. Contact: uwe.ohler@duke.edu
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影响因子：
4.3
作者：
Pruteanu-Malinici I;Mace DL;Ohler U
通讯作者：
Ohler U
影响因子：
1.6
作者：
TAUTZ, D;PFEIFLE, C
通讯作者：
PFEIFLE, C
影响因子：
5.8
作者：
Mace, Daniel L.;Varnado, Nicole;Ohler, Uwe
通讯作者：
Ohler, Uwe
影响因子：
64.5
作者：
Fowlkes, Charless C.;Luengo Hendriks, Cris L.;Malik, Jitendra
通讯作者：
Malik, Jitendra
DOI：
10.1093/bioinformatics/btq172
发表时间：
2010-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Puniyani K;Faloutsos C;Xing EP
通讯作者：
Xing EP