Knowledge Management Center for Illuminating the Druggable Genome

阐明可药物基因组的知识管理中心

• 批准号: 10598542
• 负责人: Jeremy S Edwards
• 金额: $ 94.01万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-03 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598542
• 关键词: Acceleration; Address; Adoption; Age; Alleles; Archives; Articulation; Basic Science; Biological Sciences; Biology; Biomedical Research; Chemistry; Code; Communities; Community Outreach; Computer software; Coupled; Data; Data Analytics; Data Element; Data Set; Databases; Dependence; Disease; Documentation; FAIR principles; Family; Feedback; Funding; Future; Generations; Genes; Genome; Genome Components; Genotype; Goals; Grant; Healthcare; Human; Informatics; International; Internet; Knowledge; Knowledge Management; Knowledge Management Center; Life; Link; Machine Learning; Manuals; Maps; Medicine; Metadata; Methods; Modeling; Mutation; Non-Human Protein; Ontology; Pathway interactions; Performance; Phase; Phenotype; Process; Protein Isoforms; Proteins; Proteome; Public Domains; Publications; Reagent; Research Domain Criteria; Resources; Scientist; Series; Services; Source; Structure; System; Translational Research; Trees; United States National Institutes of Health; Update; Work; analytical method; base; computer science; data ecosystem; data integration; data resource; design; disease classification; experience; gender difference; genome resource; health data; improved; interest; knowledge base; knowledge graph; knowledgebase; member; novel; online community; outreach; programs; public repository; repository; therapeutic protein; tool; web site

The main goal of the Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) program is to aggregate, update and articulate protein-centric data, information and knowledge for the entire human proteome with emphasis on understudied proteins from the 3 families that are the focus of the IDG (“IDG List”). The long-term objective of the KMC is to encourage and support biomedical research aimed at understudied proteins by providing an extensive resource of data, information, knowledge, methods and reagents for the entire human proteome, and to support the growing online community focused on understudied proteins. With focus on the IDG List and human proteins, the KMC will enable support for expanded coverage for non-human proteins of therapeutic interest and other associated human health data, in order to catalyze novel biomedical discoveries. To support the overall IDG objective, and to maintain, update and improve these integrated resources, the KMC draws upon expertise from multiple knowledge domains, specifically biology, chemistry and medicine, as well as computer science, graphic design and web programming. Specifically, for the Phase 2 of the IDG KMC we propose 4 Aims:1. Create an automated workflow that captures relevant public data for the entire proteome and manual annotations for the IDG list. The KMC knowledge management system will be built around knowledge graphs, focused on five major branches of the target knowledge tree, tkt: Genotype, Phenotype, Expression, Structure & Function, and Interactions & Pathways, respectively. Aim 2: Design, develop and implement a protein knowledgebase with Data Analytics support. Our protein-centric biomedical knowledge base, TCKB (Target Central Knowledgebase) will be comprised of the data, knowledge and information container, together with its codebase and software pipelines. TCKB will be the repository for experimental, processed and computed data and reagents originating from the IDG DRGCs (Data and Resource Generation Centers). We will provide informatics and modeling support for DRGC activities. Aim 3: We will expand, improve and maintain Pharos. Particularly “knowledge packages,” support automated data summaries for Protein Dossiers, and actively seek feedback from our community. Aim 4. Outreach to scientific community. We will support a series of activities that will leverage TCKB, Pharos and other IDG resources to increase adoption of IDG work, while observing FAIR (findable, accessible, interoperable, reusable) principles for our knowledgebase, portal and pipelines. The KMC will engage in community outreach by leading tutorials and feedback sessions and dissemination of the Pharos system. To meet its goals, the KMC will coordinate all core activities in close coordination with the IDG Steering Committee and IDG Project Scientists (PS), and include members of the IDG Consortium (IDG- C), other NIH Common Fund programs, NIH Commons, as well as other initiatives.

知识管理中心 (KMC) 的主要目标是阐明可药物基因组 (IDG) 该计划的目的是聚合、更新和阐明整个以蛋白质为中心的数据、信息和知识。 人类蛋白质组，重点关注 IDG 重点关注的 3 个家族中尚未研究的蛋白质 （“IDG 列表”） KMC 的长期目标是鼓励和支持旨在实现这一目标的生物医学研究。 通过提供广泛的数据、信息、知识、方法和资源来研究蛋白质 整个人类蛋白质组的试剂，并支持不断增长的在线社区，重点关注 KMC 将重点关注 IDG 列表和人类蛋白质，为以下领域提供支持： 扩大了具有治疗意义的非人类蛋白质和其他相关人类健康数据的覆盖范围， 为了促进新的生物医学发现，支持 IDG 的总体目标，并维护、更新。 并改进这些综合资源，KMC 借鉴多个知识领域的专业知识， 特别是生物学、化学和医学，以及计算机科学、图形设计和网络 具体来说，对于 IDG KMC 的第二阶段，我们提出了 4 个目标： 1. 创建一个自动化的项目。 捕获整个蛋白质组的相关公共数据和 IDG 列表的手动注释的工作流程。 KMC知识管理系统将围绕知识图谱构建，重点关注5大领域 目标知识树的分支，tkt：基因型、表型、表达、结构和功能，以及 分别是相互作用和途径目标 2：设计、开发和实施蛋白质知识库。 数据分析支持。我们以蛋白质为中心的生物医学知识库 TCKB（Target Central） 知识库）将由数据、知识和信息容器及其 代码库和软件管道将成为实验、处理和计算数据的存储库。 我们将提供来自 IDG DRGC（数据和资源生成中心）的试剂。 为 DRGC 活动提供信息学和建模支持 目标 3：我们将扩展、改进和维护 Pharos。 特别是“知识包”，支持蛋白质档案的自动数据摘要，并积极寻求 目标 4. 向科学界推广我们将支持一系列活动。 将利用 TCKB、Pharos 和其他 IDG 资源来提高 IDG 工作的采用率，同时观察 我们的知识库、门户和管道遵循公平（可查找、可访问、可互操作、可重用）原则。 KMC 将通过引导教程和反馈会议以及传播 为了实现其目标，KMC 将与 Pharos 系统密切协调来协调所有核心活动。 IDG 指导委员会和 IDG 项目科学家 (PS)，并包括 IDG 联盟 (IDG- C)、其他 NIH 共同基金计划、NIH Commons 以及其他举措。

项目成果

Exploring DrugCentral: from molecular structures to clinical effects.

探索 DrugCentral：从分子结构到临床效果。

• 发表时间: 2023-12
• 影响因子: 3.5
• 作者: Halip, Liliana;Avram, Sorin;Curpan, Ramona;Borota, Ana;Bora, Alina;Bologa, Cristian;Oprea, Tudor I
• 通讯作者: Oprea, Tudor I

InContext: curation of medical context for drug indications.

InContext：针对药物适应症的医学背景的管理。

• 发表时间: 2021
• 作者: Moodley, Kody;Rieswijk, Linda;Oprea, Tudor I;Dumontier, Michel
• 通讯作者: Dumontier, Michel

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

人工智能药物发现会影响临床药理学吗？

• DOI: 10.1002/cpt.1795
• 发表时间: 2020-01-19
• 期刊: Clinical Pharmacology and Therapeutics
• 影响因子: 6.7
• 作者: A. Zhavoronkov;Q. Vanhaelen;Tudor I. Oprea
• 通讯作者: Tudor I. Oprea

Autophagy and Herpesvirus: A collaboration Contributing to Alzheimer's Disease.

自噬和疱疹病毒：共同导致阿尔茨海默病。

• 发表时间: 2022-05
• 作者: Nafchi, Amir R;Esmaeili, Mona;Myers, Orrin;Oprea, Tudor;Bearer, Elane L
• 通讯作者: Bearer, Elane L

Overview of the Knowledge Management Center for Illuminating the Druggable Genome.

阐明可药物基因组的知识管理中心概述。

• 发表时间: 2024-03
• 影响因子: 7.4
• 作者: Oprea, Tudor I;Bologa, Cristian;Holmes, Jayme;Mathias, Stephen;Metzger, Vincent T;Waller, Anna;Yang, Jeremy J;Leach, Andrew R;Jensen, Lars Juhl;Kelleher, Keith J;Sheils, Timothy K;Mathé, Ewy;Avram, Sorin;Edwards, Jeremy S
• 通讯作者: Edwards, Jeremy S