标题： 基于贝叶斯中心点估计的 motifs 发现 显示英文标题

标题： Bayesian Centroid Estimation for Motif Discovery

摘要： 生物序列可能包含与重要的生物分子功能相关的模式；一个经典的例子是转录因子通过结合基因组启动子区域中的特定模式来调控基因表达。在基序发现中，我们获得了一组共享共同基序的序列，并旨在识别不仅包括基序组成，还包括该集合中每个序列的结合位点。我们提出了一种贝叶斯模型，它是Gibbs基序采样器采用模型的扩展版本，并提出了一个新的基于改进且有意义的结合位点推断损失函数的centroid估计器。我们讨论了centroid估计在基序发现中的主要优势，包括计算上的便利性，以及其原则性推导如何进一步揭示结合位点配置后验分布的更多见解。我们还使用模拟数据集和真实数据集说明了centroid估计器可以不同于最大后验估计器。 显示英文摘要

摘要： Biological sequences may contain patterns that are signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We present a Bayesian model that is an extended version of the model adopted by the Gibbs motif sampler, and propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the maximum a posteriori estimator.