Pathway Hypotheses Knowledge-base: A Knowledge Source for the Biomedical Data Translator

我们建议开发路径假说知识库(PHK)，这是一种新知识 将分析和假设由研究人员驱动的新关系和互动的来源 数据以及生物医学数据翻译器项目捕捉到的丰富知识。 能够将不同的生物医学数据知识来源整合在一起，包括 实验数据中，为了发现未知的关系已经很难实现。这个 根本的差距在于缺乏严谨和健壮的框架来链接或创建 来自不同来源的不同证据所需的复杂关系网格 知识来源。这样的框架必须解决系统的和数学驱动的问题 用于假设探索、构建和评估的算法以弥补差距， 通过评估现有资源，获取并最终发现现有来源之外的新知识 分子关系，并生成其他可操作的途径信息，这些信息可以 直接查询或通过接口查询。一个成功的框架的关键特征必须包括正式的、定义良好的 敏感度分析的机制(构造的脆弱性和可靠性的测量 假设)、影响分析(衡量重要性和知识新颖性)和简洁性 分析(假设一致性的整体测量)。最后，运行机制 新获得的知识背后必须是可逆的，并提供一个明确、准确、 来自原始知识来源的可审计来源，作为可解释性的基础。 为了实现这一愿景，PHK将采用一种称为贝叶斯的成熟人工智能知识表示法 知识库(BKB)。BKBS对知识、关系和不确定性进行建模 严格的基于图表的概率框架，能够管理不一致、不完整和 循环知识。它完全包含了各种著名的模型，包括(动态)贝叶斯模型 网络和时间表示，如隐马尔可夫模型。BKB是可以学习的 但其最关键的贡献是能够融合多个BKB及其底层 在不丢失任何信息的情况下分发。这本质上提供了端到端的转发 计算派生的后向审计，它允许准备好的灵敏度、贡献、 和影响分析。这进一步导致了探索、发现和创造的正式机制 链接多个异质知识来源的新假设。PHK将提供丰富的人工智能 随时对来自任意数量的新来源和现有来源的数据、信息和知识进行编码 包括经过管理的数据库(例如，NIH癌症基因组图谱(TCGA))和RAW实验 数据。PHK的编码使额外的知识能够通过概率和 统计推断能力。这种增强通过知识得到进一步增强 统一和融合算法将不同的知识紧密结合在一个定义良好、严格的 举止。总而言之，PHK可以系统地发现和开发新的假设。BKB有 二十多年来在多个项目中应用和部署，具有成熟的 可随时集成到翻译器框架目标原型中的软件基础。 我们的多机构团队(达特茅斯学院、塔夫茨大学临床和翻译 科学研究所(CTSI)由以下领域的高级研究人员和软件工程师组成 计算机和数据科学、化学信息学、生物信息学、分子生物学和 生物化学。小尤金·桑托斯博士。是达特茅斯大学的工程学教授。他将担任 PPI和还将领导PHK核心BKB组件的技术开发。 约瑟夫·戈姆利是塔夫茨/CTSI高级系统开发总监。他将服务于 作为本计划书下所有可交付软件的项目经理。 在此提出的PHK能力也将基于我们团队制定的战略 对于可扩展的智能信息检索，在以下情况下需要更高的透明度 对实验数据进行推理是一个主要目标。PHK将提供一个强大的新 支持两者的途径结构和分子组分的计算表示 人和机器驱动的解释和基于途径的生物标记物发现和药物 发展。PHK将实现更高效的人机联合探索和解释。

We propose to develop a Pathway Hypotheses Knowledgebase (PHK), a new knowledge source that will analyze and hypothesize novel relationships and interactions driven by researcher data together with the wealth of knowledge as captured by the Biomedical Data Translator project. The capability to bring together different biomedical data knowledge sources, including experimental data, in order to discover yet unknown relationships has been difficult to realize. The fundamental gap lies in a lack of rigorous and robust framework for linking or creating sophisticated lattices of relationships needed for different lines of evidence from heterogeneous knowledge sources. Such a framework must address systematic and mathematically driven algorithms for hypotheses exploration, construction, and assessment in order to bridge gaps, derive, and ultimately discover new knowledge beyond existing sources by assessing existing molecular relationships and generating additional actionable pathway information that can be queried directly or via API. A critical feature of a successful framework must include formal, welldefined mechanisms for sensitivity analyses (measures of fragility and reliability of constructed hypothesis), impact analyses (measures of importance and knowledge novelty), and parsimony analyses (wholistic measures of congruence of hypotheses). Lastly, the functional mechanism behind newly derived knowledge must be invertible and provide an unambiguous, precise, auditable provenance from the original knowledge sources serving as the basis for explainability. To realize this vision, PHK will employ a mature AI knowledge representation called Bayesian Knowledge Bases (BKBs). BKBs model knowledge, relationships, and uncertainty within a rigorous graph-based probabilistic framework capable of managing inconsistent, incomplete, and cyclic knowledge. It fully subsumes a variety of well-known models including (dynamic) Bayesian networks and temporal representations such as hidden Markov models. BKBs can be learned from data but its most critical contribution is the ability to fuse multiple BKBs and their underlying distributions without any loss of information. This inherently provides end-to-end forward to backward auditability of computational derivations which admits ready sensitivity, contribution, and impact analysis. This further leads to a formal mechanism to explore, discover, and create new hypotheses that links multiple heterogenous knowledge sources. PHK will provide a rich AI ready encoding of data, information, and knowledge from any number of new and existing sources including curated databases (e.g., NIH Cancer Genome Atlas (TCGA)) and raw experimental data. PHK’s encoding enables additional knowledge augmentation through probabilistic and statistical inferencing capabilities. This augmentation is further enhanced through knowledge unification and fusion algorithms closely coupling disparate knowledge in a well-defined, rigorous manner. Altogether, PHK can systematically discover and develop novel hypotheses. BKBs have been applied and deployed across a number of projects for over two decades with a mature software base for ready integration into the Translator framework’s target prototypes. Our multi-institutional team (Dartmouth College, Tufts University Clinical and Translational Sciences Institute (CTSI)) is comprised of senior researchers and software engineers in the computer and data sciences, cheminformatics, bioinformatics, molecular biology, and biochemistry. Dr. Eugene Santos Jr. is Professor of Engineering at Dartmouth. He will serve as the PI and will also lead in the technical development of the core BKB component for PHK. Joseph Gormley is the Director for Advanced Systems Development for Tufts/CTSI. He will serve as Project Manager for all software deliverables under this proposal. The PHK capabilities proposed herein will also be based on strategies developed by our team for scalable intelligent information retrieval where the desire for greater transparency when reasoning over experimental data is a primary aim. PHK will provide a powerful new computational representation of pathway structures and molecular components in support of both human and machine-driven interpretation and pathway-based biomarker discovery and drug development. PHK will enable more efficient joint human-machine exploration with explanation.