Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning.
Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning.
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DOI：
10.1093/bioinformatics/btr236
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发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Silva R
中科院分区：
文献类型：
--
作者：
Airoldi EM;Heller KA;Silva R
动机：蛋白质和蛋白质复合物协调它们的活动来执行细胞功能。在许多实验环境中，包括合成基因阵列、遗传扰动和RNAi筛选，科学家们发现了一小部分感兴趣的蛋白质相互作用。一个有效的假设是，这些相互作用是一些功能过程的可观察表型，而不是直接观察到的。验证性分析需要找到其他蛋白质对，其相互作用可能是相同功能过程的额外表型证据。现有的寻找其他蛋白质相互作用的方法严重依赖于新发现的相互作用集中的信息。例如，这些方法在监督设置中直接利用单个蛋白质的属性，以找到相关的蛋白质对。一组小的蛋白质相互作用提供了一个小样本来训练预测方法的参数，从而导致低置信度。结果：我们开发了RBSets，这是一种基于类比推理的蛋白质相互作用排序的计算方法；也就是说，学习和概括对象之间关系的能力。我们的方法是针对蛋白质相互作用的训练集很小的情况量身定制的，并间接地利用单个蛋白质的属性，在贝叶斯排名设置中，这可能是最接近数学心理学中的倾向评分。我们发现RBSets在从相互作用蛋白质的小证据集开始识别额外的相互作用方面具有良好的性能，因此可以在一定程度上建立功能过程和信号通路方面的潜在生物学逻辑。我们的方法是可伸缩的，可以以最小的计算开销应用于大型数据库。我们的研究结果表明，贝叶斯排序问题中的类比推理是实时生物发现的一种有前途的新方法。可用性：Java代码可从：www.gatsby.ucl.ac.uk/~rbas获得。联系:airoldi@fas.harvard.edu;kheller@mit.edu;ricardo@stats.ucl.ac.uk
Motivation: Proteins and protein complexes coordinate their activity to execute cellular functions. In a number of experimental settings, including synthetic genetic arrays, genetic perturbations and RNAi screens, scientists identify a small set of protein interactions of interest. A working hypothesis is often that these interactions are the observable phenotypes of some functional process, which is not directly observable. Confirmatory analysis requires finding other pairs of proteins whose interaction may be additional phenotypical evidence about the same functional process. Extant methods for finding additional protein interactions rely heavily on the information in the newly identified set of interactions. For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs. A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence. Results: We develop RBSets, a computational approach to ranking protein interactions rooted in analogical reasoning; that is, the ability to learn and generalize relations between objects. Our approach is tailored to situations where the training set of protein interactions is small, and leverages the attributes of the individual proteins indirectly, in a Bayesian ranking setting that is perhaps closest to propensity scoring in mathematical psychology. We find that RBSets leads to good performance in identifying additional interactions starting from a small evidence set of interacting proteins, for which an underlying biological logic in terms of functional processes and signaling pathways can be established with some confidence. Our approach is scalable and can be applied to large databases with minimal computational overhead. Our results suggest that analogical reasoning within a Bayesian ranking problem is a promising new approach for real-time biological discovery. Availability: Java code is available at: www.gatsby.ucl.ac.uk/~rbas. Contact: airoldi@fas.harvard.edu; kheller@mit.edu; ricardo@stats.ucl.ac.uk
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