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.

针对四种蛋白质超家族的所有预测基因组知识管理中心（IDG KMC）的数据组织核心DOC的主要目的是评估，组织和对所有前瞻性疾病连接的蛋白质进行评估：G蛋白耦合受体（GPCR），核受体（NRS），离子通道（IC）和Kinases（IC）和Kinases。作为主要知识存储库，DOC将通过结合从多个来源中提取的数据来开发“目标中央”资源数据库（TCRD），这些数据将疾病，途径，蛋白质，化学，基因，生物活性，药物发现和临床信息元素与数据库，文献，文献，专利，药物标签和其他文档相结合。 TCRD将作为IDG查询平台的中心来源，该平台由KMC的用户界面门户（UIP）Core开发。 DOC将开发用于算法处理和预测的工具，这将改善人类策划支持的疾病蛋白质关联。四个外部目标面板将策划新兴关联，对适当的蛋白质进行排名。 DOC将将蛋白质分为4种（TCLIN-临床； Tchem-由化学物质操纵； TMACRO-由大分子操纵；和Tdark-基因组“深色物质”），并由蛋白质（TTL）的组织和细胞定位数据支持（TTL）和疾病。 UNM的Oprea将领导该DOC，由团队负责人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。具体目的：1。开发用于存放在TCRD中的数据的自动提取和处理的工具； 2。为途径，疾病和相关本体学的半自动数据提取的工具开发，这将支持TTL分层； 3。开发文献和专利数据专家策划的工具，批准的药物标签和临床试验； 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。使用自动化工具，对每种蛋白质超家族进行了初步分层（例如TCLIN 22％，TDARK 30％）的疾病蛋白缔合。在12个月内，基于TCRD的IDG Querly平台将进行操作，从而改善了大型研究社区和IDG财团的目标优先级，并在探索GPCR，NRS，ICS和激酶的“暗物质”时。