Knowledge Management Center for Illuminating the Druggable Genome

摘要 未充分研究的蛋白质靶点是照亮可药物的实施阶段的重点 基因组(IDG)项目需要放在基因集/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，我们将归因于 未被充分研究的潜在靶向蛋白激酶、GPCRs和离子通道在RFA中使用机器 学习策略。为了建立这个分类系统，我们将整理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 这些属性表包括在癌细胞系(CCLE)或人类组织(GTEx)中的基因或蛋白质表达， 表达变化对药物干扰或单基因敲除(LINC)的响应，通过 基于CHIP-SEQ数据的转录因子(ENCODE)和小鼠单基因时的表型观察 被淘汰(KOMP)。总而言之，我们将处理和提取来自100多个资源的数据。然后我们将预测 靶标功能、靶标与通路的关联、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。为了进一步验证这些预测，我们将 使用文本挖掘来识别与数据挖掘预测相印证的知识，执行分子 使用同源建模对接预测的小分子，并寻找变体和 通过挖掘电子病历(EMR)以及数千个 病人。此外，我们将开发创新的数据可视化工具，使用户能够与所有 收集数据，开发社交网络软件，以建立以 蛋白质/基因/靶点以及生物学主题，包括途径、细胞类型、药物/小分子和 疾病。总体而言，我们将开发一种宝贵的资源，以加快靶向和药物发现。

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.