Data Organization Core

数据组织核心

基本信息

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
项目类别：
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

项目摘要

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的属性：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/小分子和其他扰动因素、表型/疾病/副作用以及来自患者群体的临床和基因组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/小分子和其他扰动网络、表型/疾病/副作用网络以及具有相似特征的个体患者集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，以预测针对个性化医疗中各种转化应用的待研究蛋白质的潜在适用性。