Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

生物医学数据转换器技术可行性评估和架构设计

• 批准号: 9540181
• 负责人: PAUL ANDREW CLEMONS
• 金额: $ 65.11万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-23 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9540181
• 关键词: Architecture; Artificial Intelligence; Biological; Biological Models; Biological Process; Cell Survival; Cell physiology; Cells; Clinical Trials; Communities; Complex; Computerized Medical Record; Coupled; Current Procedural Terminology Codes; DNA Sequence Alteration; Data; Data Set; Diagnosis; Diagnostic; Disease; Functional disorder; Gene Expression Profile; Generations; Genetic; Goals; Human; Human Genetics; Individual; Knowledge; Link; Methodology; Modeling; Molecular; Mutation; Nature; Organ; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Phosphorylation; Probability; Processed Genes; Property; Publishing; Quality Control; Research; Research Personnel; Resolution; Resources; Semantics; Source; Symptoms; Terminology; Tissues; Translating; Translation Process; base; biological systems; chemical genetics; data integration; design; drug candidate; experimental study; genetic association; human disease; insight; interoperability; novel; phenotypic biomarker; pre-clinical; protein function; response; small molecule; treatment effect

A fundamental challenge to translate insights between biomedical researchers, who study biological mechanisms, and clinicians, who diagnose patient symptoms, is that many links between biological processes and disease pathophysiology are poorly understood. A comprehensive Biomedical Translator must enable chains of inference across objects as diverse as genetic mutations, molecular effects, tissue-specific expression patterns, cellular processes, organ phenotypes, disease states, patient symptoms, and drug responses, a challenge beyond the scope of any one organization. Fortunately, many individual links in this chain have been made by experiments yielding statistical connections between individual data types. High-throughput perturbation screens link chemical and genetic perturbations to cellular phenotypes such as gene-expression patterns, cell survival, or changes in phosphorylation. Genetic association studies link mutations to human disease or intermediate phenotypes and biomarkers. Electronic medical records (EMR) link diseases or human phenotypes to diagnostic or current procedural terminology (CPT) codes, and clinical trials link the impact of drugs and drug candidates on disease states. In principle, incorporating these links into chains of inference could translate results between the full set of data types within them. In practice, each link is maintained by experts with domain-specific experiments, semantic terminology, and methodological standards. While a key challenge faced by a global Biomedical Translator is to establish consistent standards across these existing data types, a more important goal is to develop a principled and robust framework to (a) model biological systems and experimental approaches to investigate them; (b) organize knowledge about biological mechanism and disease; and (c) incorporate diverse datasets that serve as windows into the underlying and unknown state of nature. We propose to implement a Biomedical Translator as a probabilistic graphical model, a paradigm from artificial intelligence (AI) research. Just as separate research communities form weakly coupled parts of the translation process, graphical models allow global inferences from weakly coupled “nodes”. These inferences require each node to publish only probability distributions, enabling interoperability without necessarily having global entity-resolution standards, and benefit from paradigms for quality control, fault tolerance, and relevance assessment common in AI research. We hypothesize that a limited number of APIs, implemented as probability computations by communities around the world, would yield a Biomedical Translator as an emergent property of weakly coupled knowledge sources. From basic properties of graphical models, such a Translator could probabilistically translate among any data types connected within it, allowing for relatively complex query concepts. For example: What cellular processes in which tissues are impacted in a patient-based EMR? What genetic mutations sensitize cells to small-molecule treatment effects? Which small molecules mimic genetic “experiments of nature” that protect against disease? To illustrate the value of these resources and our architectural paradigm, we propose a demonstration project to implement a Biomedical Translator supporting queries between small molecules, biological processes, genes, and disease. The demonstration project will provide a valuable first step to confront key data-integration and organizational challenges and will enable previously impossible queries, such as identifying small molecules that perturb the same biological processes implicated by human genetics in a disease context. In this capacity, such Translator could realistically identify existing drugs for known symptoms (i.e., repurposing), but could more broadly serve as an engine for hypothesis generation and biological discovery, suggesting pre-clinical small molecules to develop based on their observed biological activity, or providing heretofore novel links between cellular protein function and disease pathophysiology.

在生物医学研究人员之间转化见解的基本挑战 生物学机制以及诊断患者症状的临床医生之间存在许多联系 生物过程和疾病病理生理学之间的关系知之甚少。一个 全面的生物医学翻译器必须启用跨对象的推理链 基因突变、分子效应、组织特异性表达模式等多种多样， 细胞过程、器官表型、疾病状态、患者症状和药物 响应，这是一项超出任何组织范围的挑战。 幸运的是，这个链条中的许多单独的环节都是通过实验产生的 各个数据类型之间的统计联系。高通量扰动屏幕 将化学和遗传扰动与细胞表型（例如基因表达）联系起来 模式、细胞存活或磷酸化的变化。遗传关联研究链接 人类疾病或中间表型和生物标志物的突变。电子医疗 记录 (EMR) 将疾病或人类表型与诊断或当前程序联系起来 术语（CPT）代码和临床试验将药物和候选药物的影响联系起来 疾病状态。 原则上，将这些链接纳入推理链可以将结果转化为 其中的完整数据类型集。在实践中，每个链接都由专家维护 特定领域的实验、语义术语和方法标准。虽然有钥匙 全球生物医学翻译人员面临的挑战是在各个领域建立一致的标准 对于这些现有的数据类型，更重要的目标是开发一个有原则的、健壮的 (a) 生物系统模型和研究它们的实验方法的框架； (b) 整理有关生物机制和疾病的知识； (c) 合并 不同的数据集作为了解潜在的和未知的自然状态的窗口。 我们建议将生物医学翻译器实现为概率图形模型， 人工智能（AI）研究的范式。正如独立的研究团体的形成一样 翻译过程中的弱耦合部分，图形模型允许从全局推断 弱耦合“节点”。这些推论要求每个节点仅发布概率 发行版，无需全局实体解析即可实现互操作性 标准，并受益于质量控制、容错和相关性的范式 人工智能研究中常见的评估。我们假设 API 数量有限， 由世界各地的社区以概率计算的形式实现，将产生 生物医学翻译器作为弱耦合知识源的新兴属性。 从图模型的基本属性来看，这样的翻译器可以概率性地翻译 在其中连接的任何数据类型之间，允许相对复杂的查询概念。为了 例如：在基于患者的 EMR 中，组织的哪些细胞过程受到影响？什么 基因突变使细胞对小分子治疗效果敏感？哪些小分子 模仿遗传“自然实验”来预防疾病？ 为了说明这些资源和我们的架构范例的价值，我们提出了一个 实施生物医学翻译器的示范项目，支持之间的查询 小分子、生物过程、基因和疾病。该示范项目将 为应对关键数据集成和组织挑战提供了宝贵的第一步， 将实现以前不可能的查询，例如识别干扰的小分子 疾病背景下人类遗传学所涉及的相同生物过程。以此身份， 这样的翻译器可以现实地识别现有药物的已知症状（即重新利用）， 但可以更广泛地充当假设生成和 生物学发现，建议根据其特性开发临床前小分子 观察到的生物活性，或提供迄今为止细胞蛋白质功能之间的新联系 和疾病病理生理学。

项目成果

Application of MCAT questions as a testing tool and evaluation metric for knowledge graph-based reasoning systems.

• DOI: 10.1111/cts.13021
• 发表时间: 2021-09
• 期刊: Clinical and translational science
• 作者: Fecho K;Balhoff J;Bizon C;Byrd WE;Hang S;Koslicki D;Rensi SE;Schmitt PL;Wawer MJ;Williams M;Ahalt SC
• 通讯作者: Ahalt SC

Knowledge Beacons: Web services for data harvesting of distributed biomedical knowledge.

• DOI: 10.1371/journal.pone.0231916
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Hannestad LM;Dančík V;Godden M;Suen IW;Huellas-Bruskiewicz KC;Good BM;Mungall CJ;Bruskiewich RM
• 通讯作者: Bruskiewich RM