Machine learning for graph-structured data: Understanding complex biological systems

许多复杂的生物和人造系统可以有效地描述为描述所涉及的元素及其相互关系的图形（网络）。例子包括但不限于：人脑、蛋白质分子、药物和化合物、电网和人类交通网络。网络科学是为了补充图论（数学的一个分支）提供的抽象框架而创建的，使其有助于研究复杂的网络系统及其时间（随时间）演化。然而，将这些系统的行为建模到微观细节并不总是可能的，首先也是最重要的，因为它们的行为和功能通常以非常重要的方式出现在相对简单的组件的相互作用中。最近提出的基于深度神经网络的机器学习方法（擅长解决历史上使用计算机难以解决的问题）允许基于图结构数据进行预测。这种有趣的方法是挑战的事实，通常情况下，存在着一个非平凡的时间依赖性之间的观察代表系统的演变。不幸的是，大多数预测行为变化的统计方法都假设没有时间顺序的独立输入。此外，这种方法通常必须在其学习中受到监督，并且在旨在预测复杂系统的行为中的异常或变化的许多应用中难以获得可靠的监督。我的长期目标是开发无监督机器学习方法，通过分析具有时间相关性的图结构数据来预测和响应复杂网络系统中的行为变化。在短期内，我的研究将包括三（3）相互关联的目标。(1)首先，我将为图结构数据设计自回归和状态空间模型（支持基于以前看到的行为预测预期的未来行为）。有了这些知识，我的团队将解决两个具有重大社会意义的应用。第一个是计算神经科学。(2)与我的学生一起，我将设计新的数据驱动模型，通过分析颅内EEG（脑活动扫描）形式的大脑记录来预测和应对耐药患者癫痫发作的发作。第二个应用是结构生物学。(3)我们还将设计其他新的数据驱动模型来处理一系列代表蛋白质-蛋白质和蛋白质-配体复合物随时间演变的图表（以帮助理解我们体内的重要功能）。这将最终导致设计出更稳定、更有效的药物。我参与拟议研究的HQP将获得先进机器学习方法及其在图结构数据中的应用方面的重要专业知识，这些技能在学术界和工业界都具有战略意义。

Many complex biological and man-made systems can efficiently be described as graphs (networks) describing the elements involved and their mutual relations. Examples include, but are not limited to: the human brain, protein molecules, drugs and chemical compounds, power grids, and human transport networks. Network science has been created to complement the abstract framework offered by graph theory (a branch of mathematics), making it useful to study complex networked systems and their temporal (over time) evolution. However, modeling the behaviour of such systems down to microscopic details is not always possible, first and foremost because their behaviour and function usually emerges in a highly non-trivial way from the interactions of relatively simple components. Recently proposed machine learning methods based on deep neural networks (which are good at solving problems that have been difficult to solve historically using computers) allow predictions to be made based on graph-structured data. This intriguing approach is challenged by the fact that, usually, there exists a non-trivial temporal dependency between the observations representing the evolution of the system. Unfortunately, most statistical methods for predicting changes in behaviour assume independent inputs that have no temporal ordering. Moreover, such methods must typically be supervised in their learning and reliable supervision is difficult to obtain in many applications aiming at predicting anomalies or changes in the behaviour of complex systems. My long-term goal is to develop unsupervised machine learning methods for predicting and reacting to behaviour changes in complex networked systems by analyzing graph-structured data having temporal correlations. In the short-term, my research will consist of three (3) interrelated objectives. (1) First, I will design autoregressive and state-space models (that support forecasting expected future behaviour based on previously seen behaviour) for graph-structured data. With this knowledge, my team will tackle two applications of great societal importance. The first is in computational neuroscience. (2) Together with my students, I will design novel data-driven models for predicting and reacting to the onset of epileptic seizures in drug-resistant patients by analyzing brain recordings in the form of intracranial EEGs (brain activity scans). The second application is in structural biology. (3) We will also design other novel data-driven models to process a sequence of graphs representing the evolution of protein-protein and protein-ligand complexes over time (to help understand important functions in our bodies). This should ultimately lead to the design of more stable and effective drugs. My HQP involved in the proposed research will gain significant expertise in advanced machine learning methods and their application for graph-structured data, skills that have become strategic both in academia and industry.