Myers Chad L, Troyanskaya Olga G
Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ, USA.
Bioinformatics. 2007 Sep 1;23(17):2322-30. doi: 10.1093/bioinformatics/btm332. Epub 2007 Jun 28.
Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context.
We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios.
A software implementation of our approach is available on request from the authors.
Supplementary data are available at http://avis.princeton.edu/contextPIXIE/
最近的几种方法通过对高通量基因组数据集中固有的噪声进行建模，解决了异构数据集成和网络预测问题，这可以显著提高特异性和灵敏度，并允许对具有异构属性的数据集进行稳健集成。然而，实验技术在捕获不同生物过程方面的成功率各不相同，因此，根据人们感兴趣预测的生物过程，每个基因组数据源的相关性可能会有所不同。考虑到这种差异可以显著改善网络预测，但据我们所知，以前没有方法明确利用这种有关生物背景的关键信息。
我们证实了功能基因组数据中存在上下文相关的差异，并提出了一种贝叶斯方法，用于对生物过程特异性网络进行上下文敏感集成和基于查询的恢复。通过将该方法应用于酿酒酵母，我们证明利用上下文信息可以显著提高网络预测的精度，包括对未表征基因的注释。我们期望这种通用的上下文敏感方法可以应用于其他生物体和预测场景。
可根据作者要求获得我们方法的软件实现。
