Investigating information flow through complex biological networks

当前研究项目的目的是使用带注释的图表示对自然界中观察到的复杂系统进行建模。然后，我们将为系统的低维非欧几里得嵌入开发新的方法，旨在保留结构和属性信息。我们希望这样的嵌入能够提供对所研究的底层系统的洞察，同时揭示其形成中的层次结构。我们还打算使用这些嵌入来更好地预测观察到的系统结构中的缺失链接，以及图中节点的缺失标签。具体来说，我们将使用源自自然语言处理的方法来嵌入接近相似节点的节点，同时使它们远离不相似节点。我们将是第一个使用属性和拓扑结构将这些技术嵌入到闵可夫斯基时空中的黎曼流形中的工作。在此之后，我们将研究可以从这些系统中提取的特征的使用-形成嵌入空间密集区域的高度相似的节点子网组。我们假设这些子网络将在分类样本的任务中有用——由于原始网络的巨大搜索空间，否则这项任务将很困难。我们将利用嵌入空间固有的层次性质将这些子网转换为决策树。使用网络来指导随机森林的构建是一个非常新的发展（Dutkowski和Ideker， 2011），然而，我们的工作将是第一个直接将先验知识纳入森林构建的方法，通过对带有属性的节点进行注释，也是第一个在此过程中使用网络表示学习技术的方法。Dutkowski， J.和Ideker， T.（2011）。蛋白质网络在发育和癌症中的逻辑功能。科学通报，7(9),391 - 391。

The aim of the current research project is to model complex systems observed in nature using annotated graph representations. We shall then develop novel methods for a low dimensional non-Euclidean embedding of the system, that aims to preserve both structural and attribute information. We hope that such embeddings are able to provide insights into the underlying systems at study but uncovering a hierarchy in their formation. We also aim to use these embeddings to better predict both missing links in the observed structure of the system as well as missing labels for the nodes within the graph.Specifically, we will use methods derived from natural language processing to embed nodes close to similar nodes, while keeping them far apart from dissimilar nodes. We shall be the first work to adapt these techniques to embed to a Reimannian manifold in Minkowski Spacetime using attributes as well as topological structure.Following this, we shall research the use of features that can be extracted from these systems - groups of highly similar subnetworks of nodes that form dense regions of the embedding space. We hypothesise that these subnetworks will be useful in the task of classifying samples - a task that would be difficult otherwise, due to the enormous search space of the original network. We shall use the inherantly hierarchical nature of the embedding space to convert these subnetworks into decision trees. Using networks to guide the construction of Random Forests is a very new development (Dutkowski and Ideker, 2011), however, our work will be the first to directly incorporate prior knowledge into the forest construction, via the annotation of nodes with attributes, and also the first method to use network representation learning techniques in this process.Dutkowski, J., & Ideker, T. (2011). Protein networks as logic functions in development and cancer. PLoS computational biology, 7(9), e1002180.