CAREER: A Data-Driven Network Inference Framework for Context-Conditioned Protein Interaction Graphs

生物系统可以作为图来研究，其中节点表示实体（例如，蛋白质）和边表示相互作用（例如，物理绑定、功能依赖性）。重要蛋白质相互作用网络的识别使人们对生命、进化变化、疾病研究和药物开发的原理有了新的认识。蛋白质相互作用图的网络连接和功能由背景决定：遗传学，环境和小分子，如药物。然而，几乎所有的蛋白质相互作用网络，到目前为止，已经检查了一个单一的静态条件下，由于生物技术的限制，图形数据收集。因此，本论文的研究目标是设计新颖有效的机器学习算法来识别上下文特异性蛋白质相互作用图。识别上下文特异性蛋白质网络具有社会重要性的生物医学应用，例如研究多个细胞阶段的细胞发育或研究白血病背景下不同药物治疗的细胞变化。这两个应用程序将通过与UVA医学院的公共卫生基因组学中心和艾米丽库里克癌症中心合作，作为该项目的评估组成部分进行探索。预计这项研究也将影响其他领域，例如社交网络发现和大脑连接的特定条件网络推理。拟议的职业计划将导致在研究的跨学科性质的基础上开展教育和外联活动。这些计划包括：（a）设计新的课程项目，研究现实生活中的网络推理问题和数据;（B）开发新的教学技术，以训练研究生的专业技能，如“如何教学”或“如何进行研究”，使用最先进的结构学习问题作为样本项目;（c）通过弗吉尼亚大学本科生顶点项目，让本科生参与网络学习研究;（d）通过在涉及高中生的UVA工程学导论（ITE）项目中的演讲，提高K-12学生对图形学习研究的认识;以及（e）加强与弗吉尼亚大学医学院社区的互动，特别是通过公开发布和教程的计算工具，从这个项目中创建。过去十年中，基因组技术发生了革命其能够同时测量数千个分子实体（例如，基因或蛋白质）。下一代测序技术产生的大量全基因组数据提供了前所未有的大规模、情境条件下的相关基因产物特征，这些特征具有很大的潜力来推断每种情境下的网络连接和功能。 该提案将开发一套新颖的机器学习方法，用于从高维、异构和噪声的多背景分子签名数据集推断特定于背景的网络。为了克服这些数据挑战，所提出的研究包括以下三个相关的任务：（i）开发新的和可扩展的结构学习算法，以从跨多个不同条件聚合的数据样本中估计多个不同但相关的稀疏高斯图形模型（sGGM），（ii）开发用于建模和检测模块的新的学习策略（即，（iii）将上述结构学习模型扩展到非高斯情况、考虑部分观察网络的半监督设置和监督疾病诊断设置。有关该项目的其他信息，包括出版物、算法的开源实现、数据集和教育材料，将通过项目网站分享：http://www.cs.virginia.edu/yanjun/context_graph/

Biological systems can be studied as graphs, where nodes represent entities (e.g., proteins) and edges represent interactions (e.g., physical binding, functional dependency). The identification of important protein interaction networks enables new insights into principles of life, evolution change, disease study, and drug development. The network wiring and function of a protein interaction graph is determined by context: genetics, environment, and small molecules such as drugs. However, almost all protein interaction networks, to date, have been examined under a single static condition, due to limitations of biotechnologies for graph data collection. Therefore, the research objective of this proposal is to design novel and efficient machine-learning algorithms to identify context-specific protein interaction graphs. Identifying context-specific protein networks has biomedical applications of social importance, such as studying cellular developments across multiple cell stages or investigating cellular changes with different drug treatments in the context of leukemia. Both applications will be explored as evaluation components of the project through collaborating with the Center for Public Health Genomics and the Emily Couric Cancer Center at UVA School of Medicine. The proposed research is expected to impact other domains as well, for instance, social-network discovery and condition-specific network inference for brain connectivity. The proposed career plan will result in educational and outreach initiatives that build on the interdisciplinary nature of the research. These plans include: (a) designing new course projects that work on real-life network-inference problems and data; (b) developing novel instructional techniques to train graduate students professional skills such as "how to teach'' or "how to do research" using state-of-the-art structural learning problems as sample projects; (c) involving undergraduates in network learning research through UVA undergraduate capstone projects; (d) increasing awareness of graph-learning research among K-12 students through presentations at the UVA Introduction to Engineering (ITE) Program involving high school students; and (e) enhancing interactions with the UVA Medical School Community, especially through public release and tutorials of computational tools created from this project.The past decade has seen a revolution in genomic technologies that enable the simultaneous measurement of thousands of molecular entities (e.g., genes or proteins). The flood of genome-wide data generated by next-generation sequencing technologies has provided an unprecedented coverage of large-scale, context-conditioned signatures of relevant gene products that have great potential to infer network connectivity and function in each context. The proposal will develop a suite of novel machine-learning methods for inference of context-specific networks from multi-context molecular signature datasets that are high dimensional, heterogeneous and noisy. Aiming to overcome these data challenges, the proposed research includes the following three related tasks: (i) develop new and scalable structural learning algorithms to estimate multiple different but related sparse Gaussian Graphical Models (sGGMs) from data samples aggregated across multiple distinct conditions, (ii) develop novel learning strategies for modeling and detecting modules (i.e., multi-protein groups) within the framework of multitasking sGGMs, (iii) extend the above structural learning models to non-Gaussian cases, semi-supervised settings considering partial-observed networks and supervised disease diagnosis settings. Additional information about the project, including the publications, open-source implementations of algorithms, data sets and educational materials will be shared through the project website: http://www.cs.virginia.edu/yanjun/context_graph/