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：我们提出了我们的翻译器推理工具mediKanren [1]的概括， “Doc Sherlock”：一个自主中继代理（ARA），可以回答来自多个 知识提供者（KPs）通过概率，逻辑和溯因推理。 问题：消除“未知已知”：随着新出版物的爆炸式增长， 数据集和发现，一个明显的现代问题已经出现： “未知的已知”未知的已知的事实要么是（1）被遗忘的事实--被公布的事实 但并不广为人知--或（2）未推断出的--从已知事实中可推断出但尚未推断出的事实。 我们把它作为KPs的角色，通过系统地收获现有的知识来发现被遗忘的东西。 知识的来源。因此，我们认为ARA的作用是解决未推断的问题， 如果所有的前提都共同居住在一个单一的， 理性的头脑。出于实用的目的，我们将查询限制为易于处理但雄心勃勃的 类：由物理学家-科学家提出的问题-对他们来说，未知已知的成本是 以患者的生命为衡量标准。 计划：Doc Sherlock将使用高级逻辑编程引擎miniKanren [2，3]，并且它将 使用概率推理规则来处理由物理学家和科学家启发的查询， 信心的结果。例如，我们想象夏洛克医生回答这个问题，“什么？ 可以治疗16p11.2缺失综合征吗“通过使用由类似Ensembl的东西支持的KP [4]， 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进行排名 单倍不足基因，例如，“KCTD 13是单倍不足”[97%置信度];基因-基因KP 像SemMedDB [7]一样找到像“KCTD 13抑制RhoA”这样的关系[97%置信度]， 然后使用药物基因KP发现“辛伐他汀抑制RhoA”[99%置信度]， 假设“辛伐他汀可能通过RhoA抑制减轻16p11.2缺失”[93%插补 信心]。为了回答来自医生科学家，数据源有基因基因， 药物-基因、疾病-基因或药物-疾病关系将是高度优先的。 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，让他们访问我们的工作，从物理学家的问题，科学家。我们将 支持ARA标准API和其他翻译标准。对于比 指定的API，我们将支持对Doc Sherlock查询语言的编程访问。 挑战：在不同的KP之间进行推理的主要技术挑战是智能地 在不同的数据集中混淆相同的概念。为了连接来自不同KP的本体， 我们建议探索伽罗瓦连接的使用[8，9] --同构的推广 适用于两个偏序（例如本体）之间。一个主要的好处将是 进行溯因推理的能力，并超越生活在一个单一的限制， KP --我们的初步数据显示，在KPs之间的推理有43%的改进[见计划]。