Data Flow across Heterogenous and Frustrated Protein Networks

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这项研究解决了生物学中的一个核心问题：从大量和多样化的实验数据中预测功能，这些数据可以识别单个细胞成分，如蛋白质，或表明它们之间的相互作用。该方法将所有此类信息建模为网络。然后将开发新的网络分析方法，以克服由于实验误差或固有的生物复杂性而导致的相互矛盾的信息。其他方法的目的是对不同类型的信息进行最佳权衡，以便将它们最好地结合在一起。其结果将在比以前可行的更大的规模上汇集生物学数据，以产生自洽和改进的蛋白质功能图片。 然而，更广泛地说，这里开发的网络分析技术应该广泛而有效地应用于任何大规模，多样化和相互冲突的数据，这是复杂系统的典型特征。具体来说，研究人员将通过沿着蛋白质图的边缘扩散不同的进化，结构和功能数据来整合来自失败网络的信息。为了科普大的网络规模，数据不一致或替代解释，半监督学习算法将被扩展，在目标1中，测试不同网络信息扩散机制下的预测准确性，在目标2中，根据替代的加权策略，汇集互补的信息网络。其成果将（1）实现具有数百万节点和边的真实生物网络，以基准计算效率;（2）基于集成的、海量的和异质的生物数据集预测蛋白质功能及其诱导的表型;以及（3），由于即使在具有自旋玻璃型挫折的网络中也将保持高计算效率，通过将基于图的半监督学习扩展到具有随机交互的复杂网络的广泛的跨学科类别，这些结果将在各种各样的领域中具有变革性。学生将在计算科学和生物学之间的接口进行培训，开发的软件工具将向研究界公开。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This study addresses a core problem in biology: to predict function from massive and diverse experimental data that identify individual cell components, such as proteins, or suggest interactions among them. The approach will model all such information as networks. New network analyses methods will then be developed to overcome conflicting information due to experimental errors or inherent biological complexities. Other methods will aim at weighing optimally different types of information so that they may be best combined together. The result will pool biological data on a dramatically larger scale than previously feasible to yield a self-consistent and improved picture of protein function. More broadly, however, the network analysis techniques developed here should apply widely and efficiently to any massive, diverse and conflicting data, typical of complex systems.Specifically, the investigators will integrate information from frustrated networks by diffusing diverse evolutionary, structural and functional data along the edges of protein graphs. To cope with the large network sizes, and data inconsistencies or alternative interpretation, semi-supervised learning algorithms will be extended, in aim 1, to test prediction accuracy under different network information diffusion mechanisms and, in aim 2, under alternative weighing strategies to pool complementary information networks. The outcome will (1) implement realistic biological networks with millions of nodes and edges to benchmark computational efficiency; (2) predict protein function and the phenotypes they induce based on integrated, massive and heterogeneous biological data sets; and (3), since high computational efficiency will be maintained even in networks with spin glass type frustration, these results will be transformative across a wide variety of fields by extending graph-based semi-supervised learning to a broad, cross-discipline class of complex networks with random interactions. Students will be trained at the interface between computational science and biology and developed software tools will be made public to the research community.