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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Biclustering with Flexible Plaid Models to Unravel Interactions between Biological Processes
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Biclustering with Flexible Plaid Models to Unravel Interactions between Biological Processes

机译：使用灵活的格子模型进行分类，以阐明生物过程之间的相互作用

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摘要

Genes can participate in multiple biological processes at a time and thus their expression can be seen as a composition of the contributions from the active processes. Biclustering under a plaid assumption allows the modeling of interactions between transcriptional modules or biclusters (subsets of genes with coherence across subsets of conditions) by assuming an additive composition of contributions in their overlapping areas. Despite the biological interest of plaid models, few biclustering algorithms consider plaid effects and, when they do, they place restrictions on the allowed types and structures of biclusters, and suffer from robustness problems by seizing exact additive matchings. We propose BiP (Biclustering using Plaid models), a biclustering algorithm with relaxations to allow expression levels to change in overlapping areas according to biologically meaningful assumptions (weighted and noise-tolerant composition of contributions). BiP can be used over existing biclustering solutions (seizing their benefits) as it is able to recover excluded areas due to unaccounted plaid effects and detect noisy areas non-explained by a plaid assumption, thus producing an explanatory model of overlapping transcriptional activity. Experiments on synthetic data support BiP’s efficiency and effectiveness. The learned models from expression data unravel meaningful and non-trivial functional interactions between biological processes associated with putative regulatory modules.
机译：基因一次可以参与多个生物过程，因此它们的表达可以看作是活跃过程中贡献的组成。在格子状假设下进行双聚簇化可以通过假设转录模块或双聚簇（在条件子集之间具有一致性的基因子集）之间的相互作用进行建模，方法是在其重叠区域中进行贡献的加法组成。尽管对格子模型有生物学上的兴趣，但几乎没有任何二类聚类算法考虑格子效果，并且当它们这样做时，它们对二类聚类的允许类型和结构施加了限制，并且由于抓住了精确的添加剂匹配而遭受了鲁棒性问题。我们提出了BiP（使用格子模型的聚类），一种双松弛的双聚类算法，可以根据生物学上有意义的假设（加权和耐噪成分的贡献）使表达水平在重叠区域发生变化。 BiP可以用于现有的双群集解决方案（抓住其优势），因为它能够恢复由于未说明的格子效果而导致的排除区域，并能够检测出格子假设无法解释的嘈杂区域，从而产生重叠转录活性的解释性模型。综合数据实验支持BiP的效率和有效性。从表达数据中学到的模型揭示了与假定的调控模块相关的生物过程之间有意义的和非平凡的功能相互作用。

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