Novel methods for integrative computational biology

综合计算生物学的新方法

• 批准号: RGPIN-2018-05757
• 负责人: Jurisica, Igor
• 金额: $ 2.99万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=654941
• 关键词: Novel; methods; integrative; computational; biology

Current challenges in biomedical research include information overload: the need to combine vast amounts of structured and unstructured information, and the need to identify, characterize, optimize, link, validate and interpret patterns in complex data. The majority of biological data and relationships between biological entities is conveniently modeled as a graph. Graph databases provide a high-performance solution to integrating diverse entities and providing a scalable platform for machine learning and inference. Data mining, graph theory and ontologies are essential components of successful learning systems. Quality, coverage and annotation of these typed graphs provide the necessary platform for model and hypotheses generation.******We propose to develop a scalable, distributed architecture to store, analyze integrated graph data to support advance machine learning and data modeling. This resource will provide comprehensive coverage of available proteome, federate and map entities to a database of experimentally detected, orthologous, and predicted tissue- and condition-specific protein interactions. We will improve and extended our association mining algorithm, FpClass, a machine learning system for weighted link prediction between interacting proteins. We will extend the method to predict links of various types between genes and proteins under the integrated graphical model in sequence, tissue, and condition-specific contexts using the Turing Complete and performance-optimized graph query language Gremlin.******Ontologies will be used to support multiple viewpoints and contexts. Graph theory and databases will provide necessary infrastructure and complex pattern inference platform. Machine learning and data mining will contribute new models, while probabilistic modeling will support handling incomplete, contradictory and ambiguous information. We will use NAViGaTOR to support visual data mining and interactive exploration of the typed graphs.******We will test and validate developed platforms using multiple, publicly available datasets. This research will generate novel algorithms, and after validation, their application will lead to improved understanding of complex diseases on a molecular level. Algorithms, results and relevant data will be made publicly available to encourage further computational research in this increasingly important area. Together with deep learning, these approaches are revolutionizing application areas since the performance of these systems frequently exceed domain experts' abilities and biological assay sensitivity and false discovery rates.******The proposed research will advance computational approaches and their applicability to high-throughput systems biology applications. An important function of this proposal is the training of bioinformatics professionals for which there is still great unfulfilled demand.**

生物医学研究中当前的挑战包括信息超载：需要结合大量结构化和非结构化信息，以及需要识别，表征，优化，链接，验证和解释复杂数据中的模式。大多数生物学数据和生物实体之间的关系都方便地建模为图。图数据库为整合不同实体并为机器学习和推理提供了可扩展的平台提供了高性能解决方案。数据挖掘，图理论和本体论是成功学习系统的重要组成部分。这些打字图的质量，覆盖范围和注释为模型和假设生成提供了必要的平台。该资源将为可用的蛋白质组，联合和地图实体提供全面的覆盖，以实验检测到的，直系同源和预测的组织和条件特异性蛋白质相互作用的数据库。我们将改进和扩展我们的关联采矿算法，FPCLASS，这是一种机器学习系统，用于相互作用蛋白质之间的加权链接预测。我们将扩展该方法，以序列，组织和条件特定的环境中的综合图形模型在基因和蛋白质之间的链接，使用图灵完整和性能优化的图形查询语言Gremlin。图理论和数据库将提供必要的基础架构和复杂的模式推理平台。机器学习和数据挖掘将贡献新的模型，而概率建模将支持处理不完整，矛盾和模棱两可的信息。我们将使用Navigator来支持类型图的视觉数据挖掘和交互式探索。******我们将使用多个公开可用的数据集测试和验证开发的平台。这项研究将产生新颖的算法，在验证后，其应用将导致对分子水平上复杂疾病的理解。将公开使用算法，结果和相关数据，以鼓励在这个日益重要的领域进行进一步的计算研究。与深度学习一起，这些方法正在彻底改变应用领域，因为这些系统的性能经常超过领域专家的能力和生物测定敏感性和虚假发现率。******拟议的研究将提高计算方法及其对高通量系统生物学应用的适用性。该提案的一个重要功能是对生物信息学专业人士的培训，这些专业人员仍然有很大的需求。**