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Knowledge Management Center for Illuminating the Druggable Genome

阐明可药物基因组的知识管理中心

基本信息

批准号：
10560469
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
项目类别：
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/10560469
关键词：
Acceleration 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 Drug Modulation G-Protein-Coupled Receptors Gene Expression Generations Genes Genome Genomics Genotype-Tissue Expression Project Goals Grant Graph Homology Modeling Human Internet Ion Channel Knock-out Knockout Mice Knowledge Knowledge Management Center Learning Link Literature Machine Learning Methods Mining Molecular Mus Online Systems Paper Pathway interactions Patients Peer Review Pharmaceutical Preparations Phase Phenotype Phosphotransferases Process Productivity Protein Kinase Proteins Public Domains Publications Publishing Reproducibility Research Personnel Resources Social Network System Time trend Tissues Transcriptional Regulation Translational Research Update Variant Visit Visualization Visualization software Work animation biobank cell type clinically relevant community building community center computerized data processing data integration data mining data visualization drug discovery druggable target human disease human tissue in silico innovation knock-down knockout gene learning strategy machine learning method machine learning prediction novel online community outreach programs protein expression repository response side effect small molecule success supervised learning text searching tool transcription factor unsupervised learning web based software web site

项目摘要

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.

总结未充分研究的蛋白质靶点是“照亮药物”计划实施阶段的重点。基因组（IDG）项目需要放在基因组/途径，药物/小分子，疾病/表型和细胞/组织。通过扩展我们以前的方法，我们将把关于使用机器研究RFA中列出的潜在靶蛋白激酶、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，将基于基因的资源转换为属性表，其中基因是行，它们的属性是列。示例包括癌细胞系（CCLE）或人组织（GTEx）中的基因或蛋白质表达，响应于药物扰动或单基因敲低（LINCS）的表达变化，基于ChIP-seq数据（ENCODE）的转录因子，以及当单个基因被淘汰（KOMP）。总的来说，我们将从100多个资源中处理和提取数据。我们将预测靶向功能、靶向与通路的关联、调节活性的小分子/药物，靶点的表达以及靶点与人类疾病的相关性。为了进一步验证这些预测，我们将使用文本挖掘来识别与数据挖掘预测相证实的知识，执行分子使用同源性建模对接预测的小分子，并寻找变体之间的关联，通过挖掘电子医疗记录（EMR）以及数千名患者此外，我们将开发创新的数据可视化工具，让用户与所有的收集数据，并开发社交网络软件，以建立以蛋白质/基因/目标以及生物学主题，包括途径，细胞类型，药物/小分子，疾病总的来说，我们将开发一种宝贵的资源，加速目标和药物的发现。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Gene and drug landing page aggregator.

DOI：
10.1093/bioadv/vbac013
发表时间：
2022
期刊：
Bioinformatics advances
影响因子：
0
作者：
Clarke DJB;Kuleshov MV;Xie Z;Evangelista JE;Meyers MR;Kropiwnicki E;Jenkins SL;Ma'ayan A
通讯作者：
Ma'ayan A

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.