Extracting biologically significant patterns from short time series gene expression data.
Extracting biologically significant patterns from short time series gene expression data.
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DOI：
10.1186/1471-2105-10-255
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发表时间：
2009-08-20
影响因子：
3
通讯作者：
Benos PV
中科院分区：
文献类型：
--
作者：
Tchagang AB;Bui KV;McGinnis T;Benos PV
时间序列基因表达数据分析被广泛用于研究各种细胞过程的动力学。目前，大多数时间序列数据只包含少数几个时间点，这使得标准聚类技术的应用变得困难。我们开发了两种新的算法，能够从短时间点序列基因表达数据中提取生物模式。这两个算法，ASTRO和MiMeSR，分别受到秩序保持框架和最小均方残差方法的启发。然而，ASTRO和MiMeSR与以前的方法不同，因为它们利用相对较少的时间点，以减少从NP难到线性的问题。在定义明确的短时间表达数据上进行测试，我们发现我们的方法对噪声和随机模式具有鲁棒性，并且它们可以正确地检测相关功能类别的时间表达谱。使用基因本体（GO）注释和染色质免疫沉淀（ChIP芯片）数据进行我们的方法的评估。我们的方法通常优于标准的聚类算法和专门为短时间序列基因表达数据聚类设计的算法。这两种算法都可以在。
Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data. Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at .
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影响因子：
5.8
作者：
Peddada, SD;Lobenhofer, EK;Umbach, DM
通讯作者：
Umbach, DM
DOI：
10.1073/pnas.101013198
发表时间：
2001-05-08
影响因子：
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DOI：
10.1073/pnas.96.6.2907
发表时间：
1999-03-16
影响因子：
11.1
作者：
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通讯作者：
Golub, TR