Data Organizing Core

数据组织核心

• 批准号: 8933011
• 负责人: TUDOR I OPREA
• 金额: $ 184.43万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8933011
• 关键词: Adult; Affect; Aging; Algorithmic Software; Algorithms; Area; Automation; Biochemistry; Bioinformatics; Biology; Biomedical Research; Categories; Cells; Chemicals; Chemistry; Clinical; Clinical Sciences; Clinical Trials; Communities; Data; Data Collection; Data Sources; Databases; Decision Making; Denmark; Deposition; Development; Dictionary; Disease; Disease Pathway; Drug Targeting; Elements; Ethnic Origin; European; Family; Feedback; Foundations; G-Protein-Coupled Receptors; Gene Proteins; Genes; Genome; Genomics; Goals; Harvest; Human; Imagery; Information Resources Management; Institutes; Ion Channel; Knowledge; Label; Lead; Legal patent; Lighting; Link; Literature; Location; Machine Learning; Manuals; Maps; Mining; Modeling; Molecular and Cellular Biology; New Mexico; Nuclear Receptors; Ontology; Organ; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacology; Phosphotransferases; Process; Proteins; Race; Regulation; Research; Research Personnel; Resources; Scheme; Scientist; Source; Stratification; Structure; System; Text; Therapeutic; Tissues; Toxicogenetics; Universities; Update; Visual; base; chemical property; computerized data processing; drug discovery; improved; interest; link protein; macromolecule; member; new technology; prospective; repository; small molecule; text searching; tool

The main goal of the Data Organizing Core, DOC, of the Illuminating the Druggable Genome Knowledge Management Center (IDG KMC) is to evaluate, organize and rank all prospective disease-linked proteins for four protein superfamilies: G-protein-coupled receptors (GPCRs), nuclear receptors (NRs), ion channels (IC) and kinases. As main knowledge repository, the DOC will develop the "Target Central" Resource Database (TCRD) by combining data extracted from multiple sources linking disease, pathway, protein, chemical, gene, bioactivity, drug discovery and clinical information elements from databases, literature, patents, drug labels and other documents. TCRD will serve as central source for the IDG Query Platform, which is developed by KMC's User Interface Portal (UIP) core. DOC will develop tools for algorithmic processing and prediction, which will improve disease-protein associations supported by human curation. Four External Target Panels will curate emerging associations, ranking appropriate proteins. DOC will stratify proteins into 4 classes (Tclin - clinical; Tchem - manipulated by chemicals; Tmacro - manipulated by macromolecules; and Tdark - the genomic "dark matter"), supported by tissue and cellular localization data for proteins (TTL) and diseases. Oprea at UNM will lead the DOC, supported by team leaders Brunak and Jensen (at Center for Protein Research, Denmark), Overington (European Bioinformatics Institute) and Schurer (University of Miami), respectively. Specific Aims: 1. Develop tools for the automated extraction and processing of data, deposited into TCRD; 2. Develop tools for the semi-automated data extraction for pathways, diseases and associated ontologies, which will support TTL stratification; 3. Develop tools for expert curation of literature and patent data, approved drug labels and clinical trials; 4. Develop analytics, modeling and visualization tools for disease-based target prioritization. Preliminary stratification (e.g., Tclin 22%, Tdark 30%) of disease-protein associations was performed for each protein superfamily, using automated tools. Within 12 months, the TCRD-based IDG Querly Platform will be operational, improving target prioritization for the research community at large and the IDG Consortium, in exploring "dark matter" for GPCRs, NRs, ICs and kinases.

照亮可药物基因组知识管理中心 (IDG KMC) 的数据组织核心 (DOC) 的主要目标是评估、组织和排序四个蛋白质超家族的所有潜在疾病相关蛋白质：G 蛋白偶联受体 (GPCR) ）、核受体（NR）、离子通道（IC）和激酶。作为主要的知识库，DOC 将通过结合从多个来源提取的数据来开发“目标中央”资源数据库（TCRD），这些数据涉及数据库、文献、疾病、途径、蛋白质、化学、基因、生物活性、药物发现和临床信息元素。专利、药品标签和其他文件。 TCRD 将作为 IDG 查询平台的中心源，该平台由 KMC 的用户界面门户 (UIP) 核心开发。 DOC 将开发算法处理和预测工具，这将改善人类管理支持的疾病与蛋白质关联。四个外部目标小组将策划新兴的关联，对适当的蛋白质进行排名。 DOC 将蛋白质分为 4 类（Tclin - 临床；Tchem - 由化学物质操纵；Tmacro - 由大分子操纵；Tdark - 基因组“暗物质”），并得到蛋白质 (TTL) 和疾病的组织和细胞定位数据的支持。新墨西哥大学的 Oprea 将领导 DOC，并分别得到团队领导 Brunak 和 Jensen（丹麦蛋白质研究中心）、Overington（欧洲生物信息学研究所）和 Schurer（迈阿密大学）的支持。具体目标： 1. 开发自动提取和处理数据的工具，存入 TCRD； 2. 开发用于路径、疾病和相关本体的半自动数据提取工具，这将支持 TTL 分层； 3. 开发文献和专利数据、批准的药物标签和临床试验的专家管理工具； 4. 开发分析、建模和可视化工具，用于基于疾病的目标优先级排序。使用自动化工具对每个蛋白质超家族进行疾病-蛋白质关联的初步分层（例如，Tclin 22%、Tdark 30%）。 12 个月内，基于 TCRD 的 IDG Querly 平台将投入运行，改善整个研究界和 IDG 联盟的目标优先顺序，探索 GPCR、NR、IC 和激酶的“暗物质”。