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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Analyzing gene expression time-courses
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Analyzing gene expression time-courses

机译：分析基因表达的时程

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摘要

Measuring gene expression over time can provide important insights into basic cellular processes. Identifying groups of genes with similar expression time-courses is a crucial first step in the analysis. As biologically relevant groups frequently overlap, due to genes having several distinct roles in those cellular processes, this is a difficult problem for classical clustering methods. We use a mixture model to circumvent this principal problem, with hidden Markov models (HMMs) as effective and flexible components. We show that the ensuing estimation problem can be addressed with additional labeled data partially supervised learning of mixtures - through a modification of the expectation-maximization (EM) algorithm. Good starting points for the mixture estimation are obtained through a modification to Bayesian model merging, which allows us to learn a collection of initial HMMs. We infer groups from mixtures with a simple information-theoretic decoding heuristic, which quantifies the level of ambiguity in group assignment. The effectiveness is shown with high-quality annotation data. As the HMMs we propose capture asynchronous behavior by design, the groups we find are also asynchronous. Synchronous subgroups are obtained from a novel algorithm based on Viterbi paths. We show the suitability of our HMM mixture approach on biological and simulated data and through the favorable comparison with previous approaches. A software implementing the method is freely available under the GPL from http://ghmm.org/gql.
机译：随着时间的推移测量基因表达可以提供对基本细胞过程的重要见解。鉴定具有相似表达时程的基因组是分析中至关重要的第一步。由于生物学相关的基团经常重叠，由于基因在那些细胞过程中具有几种不同的作用，所以这对于经典的聚类方法来说是一个难题。我们使用混合模型来规避这个主要问题，将隐马尔可夫模型（HMM）作为有效且灵活的组件。我们表明，可以通过对期望最大化（EM）算法的修改，通过部分监督混合物学习的附加标记数据来解决随后的估计问题。通过修改贝叶斯模型合并可以获得混合估计的良好起点，这使我们能够学习初始HMM的集合。我们使用简单的信息理论解码试探法从混合物中推断出组，从而量化了组分配中的歧义级别。有效性通过高质量注释数据显示。当我们建议HMM通过设计捕获异步行为时，我们发现的组也是异步的。同步子组是从基于维特比路径的新颖算法中获得的。我们通过与以前的方法进行了有益的比较，证明了我们的HMM混合方法在生物学和模拟数据上的适用性。根据GPL，可以从http://ghmm.org/gql免费获得实现该方法的软件。

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