Associative clustering for exploring dependencies between functional genomics data sets

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.     

microRNA生物合成的调控进展——TGFβ/Smad信号途径的作用

microRNA（miRNA）是一类非编码的小分子单链RNA,主要通过转录后抑制靶基因表达调节各种生物功能。miRNA表达水平在正常发育过程中以及在癌症及心血管疾病等各种疾病中发生变化。目前控制miRNA生物合成的信号途径以及调节机制尚未完全阐明。miRNA基因转录后在大蛋白复合体的介导下经一系列协调加工过程形成成熟的miRNA。近来发现转化生长因子β（TGFβ）信号途径的转导因子Smad蛋白在细胞核内调节miRNA的加工处理过程起重要作用。本文主要综述TGFβ/Smad信号途径在miRNA生物合成中的调节作用。