Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA.
Bioinformatics. 2011 Jul 1;27(13):i374-82. doi: 10.1093/bioinformatics/btr236.
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.
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.
Java code is available at: www.gatsby.ucl.ac.uk/~rbas.
airoldi@fas.harvard.edu; kheller@mit.edu; ricardo@stats.ucl.ac.uk.
蛋白质和蛋白质复合物协调其活性以执行细胞功能。在许多实验环境中，包括合成遗传阵列、遗传扰动和 RNAi 筛选，科学家们确定了一小部分感兴趣的蛋白质相互作用。一个工作假设通常是，这些相互作用是一些功能过程的可观察表型，而该功能过程是不可直接观察的。验证性分析需要找到其他对蛋白质，其相互作用可能是关于相同功能过程的另外的表型证据。现有的寻找额外蛋白质相互作用的方法严重依赖于新确定的相互作用集中的信息。例如，这些方法在监督环境中直接利用个体蛋白质的属性，以找到相关的蛋白质对。一小部分蛋白质相互作用提供了一个小样本，可以训练预测方法的参数，从而导致置信度低。
我们开发了 RBSets，这是一种基于类比推理的排名蛋白质相互作用的计算方法；也就是说，学习和泛化对象之间关系的能力。我们的方法适用于蛋白质相互作用的训练集很小的情况，并在贝叶斯排名设置中间接利用个体蛋白质的属性，这种设置在数学心理学中可能最接近倾向评分。我们发现，RBSets 可以在从一小部分相互作用的蛋白质证据集开始识别额外的相互作用方面取得良好的性能，对于这些相互作用，可以基于功能过程和信号通路的生物学逻辑建立一定的置信度。我们的方法具有可扩展性，可以应用于具有最小计算开销的大型数据库。我们的结果表明，贝叶斯排名问题中的类比推理是实时生物学发现的一种很有前途的新方法。
Java 代码可在：www.gatsby.ucl.ac.uk/~rbas. 获得。
airoldi@fas.harvard.edu; kheller@mit.edu; ricardo@stats.ucl.ac.uk。
