Knowledge-Based Biomedical Data Science

基于知识的生物医学数据科学 在之前的资助期间，我们设计和构建了突破性的方法，以创建 通过自动转换和实现语义连贯和逻辑一致的知识库 整合了许多生物医学数据库，并直接从文献中提取信息。 建立在几十年的生物医学本体开发工作的基础上，并利用体系结构 支持语义网，我们已经演示了允许有效查询跨越的方法 纯生物学术语中的数据源的任何组合，而无需查询反映 关于任何来源的信息的结构或分布的任何信息。这些方法 还能够以逻辑一致的方式表示明显相互冲突的信息，以及 跟踪知识库中所有断言的来源。也许最重要的特点是 这些方法的特点是，它们的规模可能包括几乎所有的分子生物学知识。 我们现在假设，使用这些技术，我们可以建立足够广泛的知识库 覆盖范围以克服阻碍以前的象征性人工处理方法的“脆性”问题 智能，然后创造新的计算方法，利用这些知识提供 用于解释和分析生物医学数据的关键新工具。为了检验这一假设，我们 建议解决以下具体目标： 1.确定基于知识的数据科学中具有代表性和重要的分析需求，以及 完善和扩展我们的知识库，以满足以下三个不同领域的需求：临床 药理学、心血管疾病和罕见的遗传病。 2.开发新的并实现现有的符号、统计、基于网络的机器学习 以及从非常大的知识库中进行目标驱动推理的混合方法。创建一个目标-- 选择和组合这些推理方法以解决特定问题的指导框架 分析问题。 3.克服外部广泛采用已开发方法的障碍，通过分析其 计算复杂性，优化基于知识的查询和推理的性能， 开发简化的、以生物为中心的查询语言，对知识进行轻量级打包 资源和系统，并解决许可和数据再分配问题。

Knowledge-based biomedical data science In the previous funding period, we designed and constructed breakthrough methods for creating a semantically coherent and logically consistent knowledge-base by automatically transforming and integrating many biomedical databases, and by directly extracting information from the literature. Building on decades of work in biomedical ontology development, and exploiting the architectures supporting the Semantic Web, we have demonstrated methods that allow effective querying spanning any combination of data sources in purely biological terms, without the queries having to reflect anything about the structure or distribution of information among any of the sources. These methods are also capable of representing apparently conflicting information in a logically consistent manner, and tracking the provenance of all assertions in the knowledge-base. Perhaps the most important feature of these methods is that they scale to potentially include nearly all knowledge of molecular biology. We now hypothesize that using these technologies we can build knowledge-bases with broad enough coverage to overcome the “brittleness” problems that stymied previous approaches to symbolic artificial intelligence, and then create novel computational methods which leverage that knowledge to provide critical new tools for the interpretation and analysis of biomedical data. To test this hypothesis, we propose to address the following specific aims: 1. Identify representative and significant analytical needs in knowledge-based data science, and refine and extend our knowledge-base to address those needs in three distinct domains: clinical pharmacology, cardiovascular disease and rare genetic disease. 2. Develop novel and implement existing symbolic, statistical, network-based, machine learning and hybrid approaches to goal-driven inference from very large knowledge-bases. Create a goal- directed framework for selecting and combining these inference methods to address particular analytical problems. 3. Overcome barriers to broad external adoption of developed methods by analyzing their computational complexity, optimizing performance of knowledge-based querying and inference, developing simplified, biology-focused query languages, lightweight packaging of knowledge resources and systems, and addressing issues of licensing and data redistribution.