Data Organization Core

数据组织核心

• 批准号: 8934410
• 负责人: Joel Thomas Dudley
• 金额: $ 44.92万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 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/8934410
• 关键词: Adverse effects; Algorithms; Apache Indians; Biology; Biotechnology; Classification; Clinical; Clinical Data; Cluster Analysis; Code; Comorbidity; Complex; Cyclic GMP-Dependent Protein Kinases; Data; Data Collection; Data Set; Databases; Disease; Drug Targeting; Family; G-Protein-Coupled Receptors; Gene Expression; Gene Expression Profile; Gene Proteins; Gene Targeting; Genes; Genome; Genomics; Goals; Individual; Information Resources Management; Ion Channel; Knock-out; Knowledge; Letters; Link; Literature; Machine Learning; Maps; Measurement; Medicine; Methods; Molecular Profiling; Mutation; Nuclear Receptors; Ontology; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Process; Property; Proteins; Proteomics; Regulation; Research; Running; Science; Structure; System; The Cancer Genome Atlas; Training; United States National Institutes of Health; base; biomedical ontology; cluster computing; cohort; data integration; disease phenotype; exome sequencing; genome-wide; histone modification; indexing; novel; open source; parallel processing; protein protein interaction; protein structure; small molecule; technology development; three dimensional structure; tool; transcription factor

The Data Organization Core (DOC) of the Mount Sinai's KMC-IDG will collect, process, and maintain attributes about the druggable targets for all proposed families: protein kinases, G-protein coupled receptors, nuclear receptors and ion channels. The emphasis will be to focus on those genes/proteins that are understudied and collect unbiased genome-wide profiling datasets. In addition, the DOC will collect, process and maintain data tables and attributes for all other genes/proteins, drugs/small-molecules and other perturbagens, pheontypes/diseases/side-effects, and clinical as well as genomics datasets from cohorts of patients. This will enable us to identify links between and across genes/proteins networks, drugs/small-molecules and other perturbagens networks, pheontypes/diseases/side-effects networks, and clusters of individual patients with similar profiles. For this, the Core will develop and apply clustering and classification algorithms as well as workflows to make predictions about the potential applicability of targeting the understudied proteins for various translational applications in personalized medicine.

西奈山KMC-IDG的数据组织核心（DOC）将收集、处理和维护所有拟议家族的可药物靶标属性：蛋白激酶、g蛋白偶联受体、核受体和离子通道。重点将放在那些未被充分研究的基因/蛋白质上，并收集无偏见的全基因组分析数据集。此外，DOC将收集、处理和维护所有其他基因/蛋白质、药物/小分子和其他扰动原、表型/疾病/副作用以及来自患者队列的临床和基因组学数据集的数据表和属性。这将使我们能够识别基因/蛋白质网络、药物/小分子和其他摄动原网络、表型/疾病/副作用网络以及具有相似概况的个体患者群之间和之间的联系。为此，核心将开发和应用聚类和分类算法以及工作流程，以预测针对未充分研究的蛋白质在个性化医疗中的各种转化应用的潜在适用性。