Inferring genetic regulatory logic from expression data
Inferring genetic regulatory logic from expression data
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DOI：
10.1093/bioinformatics/bti388
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发表时间：
2005-06-01
期刊：
影响因子：
5.8
通讯作者：
Eils, R
中科院分区：
文献类型：
--
作者：
Bulashevska, S;Eils, R
动机：高通量分子遗传学方法可以收集不同时间点和不同条件下基因表达的数据。挑战在于从这些数据中推断基因调控的相互作用，并深入了解基因调控的机制。结果：我们提出了一个遗传调控相互作用的模型，该模型具有生物动机的布尔逻辑语义，但具有概率性，因此能够面对嘈杂的生物过程和数据。我们提出了一种基于贝叶斯方法和吉布斯抽样的模型学习方法。我们用先前发表的酿酒酵母细胞周期数据测试了我们的方法，发现了与生物学知识一致的基因之间的关系。
Motivation: High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation.Results: We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.