Integrating Microarray and Proteomic Data by Ontology-based Annotation

通过基于本体的注释整合微阵列和蛋白质组数据

• 批准号: 7693803
• 负责人: ATUL J BUTTE
• 金额: $ 28万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7693803
• 关键词: Address; Advisory Committees; Automobile Driving; Biological; Biological Process; Cells; Cellular biology; Chemicals; Classification; Clinical; Computer software; Data; Data Set; Databases; Detection; Development; Disease; Ensure; Funding; Gene Expression; Genetic Transcription; Genome; Genomics; Growth; Head; Human Genome Project; Improve Access; International; Investments; Machine Learning; Manuals; Maps; Measurement; Methods; Molecular Biology; Nature; Online Systems; Ontology; Organ Transplantation; Phenotype; Play; Process; Proteomics; Publications; Research; Research Personnel; Role; Sampling; Scientist; Sensitivity and Specificity; Specificity; System; T-Lymphocyte; Text; Time; Translating; Translations; Transplantation; Unified Medical Language System; United States National Institutes of Health; United States National Library of Medicine; Writing; base; bench to bedside; biomedical informatics; biomedical ontology; genome-wide; improved; malignant breast neoplasm; repository; research study; text searching; tool; translational medicine

DESCRIPTION (provided by applicant): With the completion of the Human Genome Project, there is a need to translate genome-era discoveries into clinical utility. One difficulty in making bench-to-bedside translations with gene-expression and proteomic data is our current inability to relate these findings with each other and with clinical measurements. A translational researcher studying a particular biological process using microarrays or proteomics will want to gather as many relevant publicly-available data sets as possible, to compare findings. Translational investigators wanting to relate clinical or chemical data with multiple genomic or proteomic measurements will want to find and join related data sets. Unfortunately, finding and joining relevant data sets is particularly challenging today, as the useful annotations of this data are still represented only by unstructured free-text, limiting its secondary use. A question we have sought to answer is whether prior investments in biomedical ontologies can provide leverage in determining the context of genomic data in an automated manner, thereby enabling integration of gene expression and proteomic data and the secondary use of genomic data in multiple fields of research beyond those for which the data sets were originally targeted. The three specific aims to address this question are to (1) develop tools that comprehensively map contextual annotations to the largest biomedical ontology, the Unified Medical Language System (UMLS), built and supported by the National Library of Medicine, validate, and disseminate the mappings, (2) execute a four-pronged strategy to evaluate experiment-concept mappings, and (3) apply experiment-context mappings to find and integrate data within and across microarray and proteomics repositories. To keep these tools relevant to biomedical investigators, we have included three Driving Biological Projects (DBPs), in the domains of breast cancer, organ transplantation, and T-cell biology. To accomplish these DBPs, our tools and mappings will be used to find and join experimental data within and across microarray and proteomic repositories. Having DBPs to address will focus our development on a set of scalable tools that can access and analyze experimental data covering a large variety of diseases. Through our advisory committee of world-renowned NIH-funded investigators, we will ensure that our findings will have broad applicability and are useful to a wide variety of biomedical researchers.

描述（由申请人提供）： 随着人类基因组计划的完成，需要将基因组时代的发现转化为临床应用。利用基因表达和蛋白质组数据进行实验室到临床转化的一个困难是我们目前无法将这些发现相互关联以及与临床测量关联起来。使用微阵列或蛋白质组学研究特定生物过程的转化研究人员希望收集尽可能多的相关公开数据集，以比较研究结果。想要将临床或化学数据与多种基因组或蛋白质组测量联系起来的转化研究人员将希望找到并加入相关数据集。不幸的是，今天查找和加入相关数据集特别具有挑战性，因为这些数据的有用注释仍然仅由非结构化自由文本表示，限制了其二次使用。我们试图回答的一个问题是，先前对生物医学本体的投资是否可以提供以自动化方式确定基因组数据背景的杠杆作用，从而实现基因表达和蛋白质组数据的整合，以及基因组数据在数据集最初目标之外的多个研究领域的二次使用。解决这个问题的三个具体目标是（1）开发工具，将上下文注释全面映射到最大的生物医学本体，即由国家医学图书馆建立和支持的统一医学语言系统（UMLS），验证和传播映射，（2）执行四管齐下的策略来评估实验概念映射，以及（3）应用实验上下文映射来查找和集成数据。 微阵列和蛋白质组学存储库。为了使这些工具与生物医学研究人员保持相关性，我们在乳腺癌、器官移植和 T 细胞生物学领域纳入了三个驱动生物学项目 (DBP)。为了实现这些 DBP，我们的工具和映射将用于查找和连接微阵列和蛋白质组存储库内和之间的实验数据。解决 DBP 问题将使我们的开发重点集中在一组可扩展的工具上，这些工具可以访问和分析涵盖多种疾病的实验数据。通过由美国国立卫生研究院 (NIH) 资助的世界知名研究人员组成的咨询委员会，我们将确保我们的研究结果具有广泛的适用性，并对各种生物医学研究人员有用。