Doc Sherlock: An Autonomous Relay Agent for Discovering the "Unknown Knowns" in Precision Medicine

夏洛克博士：发现精准医学中“未知的知识”的自主中继代理

• 批准号: 10333495
• 负责人: William E Byrd
• 金额: $ 92.23万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-24 至 2022-01-23
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10333495
• 关键词: 16p11.2; Back; Collaborations; Data; Data Discovery; Data Set; Data Sources; Disease; Genes; Harvest; Intelligence; Knowledge; Language; Logic; Measures; Mind; Modeling; Modernization; Ontology; Patients; Pharmaceutical Preparations; Phase; Physicians; Provider; Publications; Publishing; Role; Scientist; Simvastatin; Site; Source; Syndrome; Visit; Work; cost; new growth; precision medicine; tool

Component: We propose a generalization of our Translator reasoner tool mediKanren [1] into “Doc Sherlock”: an Autonomous Relay Agent (ARA) to answer biomedical queries from multiple Knowledge Providers (KPs) via probabilistic, logical and abductive inference. Problem: Eliminating the “Unknown Known”: With the explosive growth of new publications, data sets and discoveries, a distinctly modern problem has emerged: the rapid expansion of the “unknown known.” The unknown known are facts that are either (1) forgotten -- facts published but not known widely -- or (2) uninferred -- facts inferable from known facts but not yet deduced. We take it as the role of KPs to uncover the forgotten by systematically harvesting existing sources of knowledge. We thus take it as the role of ARAs to tackle the uninferred -- the conclusions that could have been drawn if only all of the premises were co-resident in a single reasoner’s mind. For pragmatic purposes, we restrict the queries to a tractable yet ambitious class: queries raised by physician-scientists -- for whom the cost of the unknown known is measured in patients' lives. Plan : Doc Sherlock will use the advanced logic programming engine miniKanren [2,3] , and it will use probabilistic inference rules to tackle queries inspired by physician-scientists and rank results by confidence. For example, we imagine Doc Sherlock answering the question, “What may treat 16p11.2 deletion syndrome?” by using a KP backed by something like Ensembl [4] to look up all the genes in 16p11.2; a second KP backed by a dataset like gnomAD [5,6] to rank haploinsufficient genes, e.g., “KCTD13 is haploinsufficient” [97% confidence]; a gene-gene KP like SemMedDB [7] to find a relationship like “KCTD13 inhibits RhoA” [97% confidence] and then using a drug-gene KP to find that “Simvastatin inhibits RhoA” [99% confidence] to hypothesize that “Simvastatin may mitigate 16p11.2 deletion via RhoA inhibition” [93% imputed confidence]. To answer queries from physician-scientists, data sources which have gene-gene, drug-gene, disease-gene or drug-disease relationships will be high priority. Collaboration : Building on our collaboration model from the current Translator phase, we plan to visit other sites and have them visit us to work on problems from physician-scientists. We will support the ARA standard API, and other Translator Standards. For queries richer than the designated API, we will support programmatic access to Doc Sherlock’s query language. Challenges: The primary technical challenge in reasoning across distinct KPs is intelligently aliasing identical concepts within different data sets. To connect ontologies from different KPs, we propose exploring the use of Galois connections [8,9] -- a generalization of isomorphism suitable for use between two partial orders (such as ontologies). A major benefit will be the ability to conduct abductive reasoning and to go beyond the restrictions of living within a single KP -- our preliminary data shows a 43% improvement in inference across KPs [see Plan].

组件：我们提出了将翻译人员推理工具medikanren [1]的概括 “ Doc Sherlock”：自主接力代理（ARA），以回答多个生物医学查询 通过概率，逻辑和绑架推理，知识提供者（KPS）。 问题：消除“未知”：随着新出版物的爆炸性增长， 数据集和发现，出现了一个明显的现代问题： “未知。”未知的事实是（1）被遗忘的事实 - 发表的事实 但尚不广泛地知道 - 或（2）未经向前的事实，可以从已知的事实中推断出来，但尚未推论。 我们将其作为KP的角色，以系统地收获现有 知识来源。因此，我们将其作为Aras的角色来解决未经的信息 - 如果只有所有房屋是共同居住的一个结论，则可以得出的结论 推理者的想法。出于务实的目的，我们将查询限制在一个可行的但雄心勃勃的 班级：物理科学家提出的查询 - 未知的成本是 在患者的生活中衡量。 计划：Doc Sherlock将使用高级逻辑编程引擎Minikanren [2,3]，它将 使用概率推理规则来解决受物理科学家启发的查询并等级 通过信心结果。例如，我们想象Doc Sherlock回答了一个问题：“什么 可以治疗16p11.2删除综合征吗？ 查找16p11.2中的所有基因；由GNOMAD [5,6]等数据集支持的第二个KP来排名 单倍弥补的基因，例如，“ KCTD13是单倍弥补的” [97％的置信度]； Gene-Gene KP 像semmeddb [7]一样，可以找到“ kctd13抑制rhoa”之类的关系[97％的置信度]和 然后使用药物基因KP发现“辛伐他汀抑制Rhoa” [99％的置信度] 假设“辛伐他汀可以通过RhoA抑制减轻16p11.2删除” [93％估算 信心]。为了回答物理科学家的查询，具有基因基因的数据源， 毒品，疾病 - 基因或药物疾病关系将是很高的优先级。 协作：基于当前翻译阶段的协作模型，我们计划 访问其他网站并让他们访问我们，以解决身体科学家的问题。我们将 支持ARA标准API和其他翻译标准。查询比 指定的API，我们将支持对Doc Sherlock查询语言的程序化访问。 挑战：跨不同KP的推理的主要技术挑战是明智的 在不同的数据集中混合相同的概念。要连接来自不同KPS的本体论， 我们建议探索Galois连接的使用[8,9] - 同构的概括 适用于两个部分订单之间的使用（例如本体论）。主要好处将是 绝对推理，超越了一个单一生活的限制 KP-我们的初步数据显示，KPS的推理提高了43％[请参阅计划]。