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: 10314036
• 负责人: Avi Ma'ayan
• 金额: $ 25万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-08 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10314036
• 关键词: Achievement; Award; Biological; Cancer cell line; Cells; ChIP-seq; Classification; Communication; Communities; Complex; Computer software; Computerized Medical Record; Data; Data Collection; Data Set; Databases; Development; Disease; Docking; G-Protein-Coupled Receptors; Gene Expression; Gene Proteins; Gene Targeting; Generations; Genes; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Grant; Graph; Homology Modeling; Human; Internet; Ion Channel; Knock-out; Knockout Mice; Knowledge; Knowledge Management; Learning; Link; Literature; Machine Learning; Methods; Mining; Molecular; Mus; Online Systems; Paper; Pathway interactions; Patients; Peer Review; Pharmaceutical Preparations; Phase; Phenotype; Phosphotransferases; Process; Protein Kinase; Proteins; Public Domains; Publications; Publishing; Reproducibility; Research Personnel; Resources; Social Network; Supervision; System; Time trend; Tissues; Transcriptional Regulation; Translational Research; Update; Variant; Visit; Visualization software; Work; base; biobank; cell type; clinically relevant; community center; computerized data processing; data integration; data mining; data resource; data visualization; disease phenotype; drug discovery; druggable target; human disease; human tissue; in silico; innovation; knock-down; learning strategy; machine learning method; novel; online community; outreach; programs; protein expression; repository; response; side effect; small molecule; success; text searching; tool; transcription factor; unsupervised learning; web site

SUMMARY The understudied protein targets that are the focus of the implementation phase of the Illuminating the Druggable Genome (IDG) project need to be placed in the contexts of gene-sets/pathways, drugs/small-molecules, diseases/phenotypes, and cells/tissues. By extending our previous methods, we will impute knowledge about the understudied potential target protein kinases, GPCRs, and ion channels listed in the RFA using machine learning strategies. To establish this classification system, we will organize data from many omics- and literature- based resources into attribute tables where genes are the rows and their attributes are the columns. Examples of such attribute tables include gene or protein expression in cancer cell lines (CCLE) or human tissues (GTEx), changes in expression in response to drug perturbations or single-gene knockdowns (LINCS), regulation by transcription factors based on ChIP-seq data (ENCODE), and phenotypes in mice observed when single genes are knocked out (KOMP). In total, we will process and abstract data from over 100 resources. We will then predict target functions, target association with pathways, small-molecules/drugs that modulate the activity and expression of the target, and target relevance to human disease. To further validate such predictions, we will employ text mining to identify knowledge that corroborates with the data mining predictions, perform molecular docking of predicted small molecules using homology modeling, and seek associations between variants and human diseases by mining electronic medical records (EMR) together with genomic profiling of thousands of patients. In addition, we will develop innovative data visualization tools to allow users to interact with all the collected data, and develop social networking software to build communities centered around proteins/genes/targets as well as biological topics including pathways, cell types, drugs/small-molecules, and diseases. Overall, we will develop an invaluable resource that will accelerate target and drug discovery.

概括 未充分研究的蛋白质靶点是“照亮药物”实施阶段的重点 基因组（IDG）项目需要置于基因集/通路、药物/小分子、 疾病/表型和细胞/组织。通过扩展我们之前的方法，我们将估算关于 使用机器在 RFA 中列出的待研究的潜在靶蛋白激酶、GPCR 和离子通道 学习策略。为了建立这个分类系统，我们将组织来自许多组学和文献的数据 基于资源的属性表，其中基因是行，它们的属性是列。示例 这些属性表包括癌细胞系（CCLE）或人体组织（GTEx）中的基因或蛋白质表达， 响应药物扰动或单基因敲低（LINCS）的表达变化，通过调节 基于 ChIP-seq 数据 (ENCODE) 的转录因子，以及单基因时观察到的小鼠表型 被淘汰（KOMP）。总的来说，我们将从 100 多个资源中处理和提取数据。然后我们将预测 目标功能、与途径的目标关联、调节活性的小分子/药物以及 靶标的表达以及靶标与人类疾病的相关性。为了进一步验证此类预测，我们将 采用文本挖掘来识别与数据挖掘预测相符的知识，执行分子分析 使用同源模型对接预测的小分子，并寻找变体和 通过挖掘电子病历 (EMR) 以及数千个基因组分析来诊断人类疾病 患者。此外，我们将开发创新的数据可视化工具，让用户能够与所有 收集数据，并开发社交网络软件，建立以 蛋白质/基因/靶标以及生物主题，包括途径、细胞类型、药物/小分子和 疾病。总体而言，我们将开发宝贵的资源，以加速靶标和药物的发现。