Knowledge Management Center for Illuminating the Druggable Genome

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， 靶标的表达以及靶标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。

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.