Immune System Biological Networks:Case Study Improved Data Integration & Analysis

免疫系统生物网络：案例研究改进的数据集成

• 批准号: 8147764
• 负责人: LINDSAY G. COWELL
• 金额: $ 33.61万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-22 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8147764
• 关键词: Acute; Area; Bacteremia; Biological; Biology; Biomedical Research; Blood Circulation; Candidate Disease Gene; Case Study; Cells; Clinical; Clinical Data; Clinical Research; Collaborations; Communities; Data; Data Analyses; Development; Disease; Foundations; Genes; Genotype; Goals; Immune response; Immune system; Infection; Infectious Diseases Research; Information Resources; Knowledge; Link; Methods; Molecular; Ontology; Organism; Outcome; Pathogenesis; Pathway interactions; Patients; Process; Research; Resources; Retrieval; Sample Size; Source; Staphylococcus aureus; Structure; System; Taxonomy; Testing; Translational Research; Translations; base; clinical practice; complex biological systems; data integration; design; improved; information organization; new therapeutic target; novel diagnostics; novel therapeutics; pathogen; success; syntax; translational medicine

DESCRIPTION (provided by applicant): Progress in biomedical research and its translation into clinical practice require the integration of data across multiple scales (molecules, cells, organisms), organism types, and fields of research. The need for data integration is especially acute in infectious disease research where organisms interact on all scales, and these interactions result in the emergence of processes and structures specific to these interactions. True data integration, the ability to jointly interpret and analyze data of heterogeneous types, depends on the ability to link data to information about the biological entities to which the data refer. In the face of rapidly growing volumes of data and information, it is imperative that this link from data to information be computable. Automated processing of the links between data and information requires that they be expressed using a common, formalized system for knowledge representation. Efforts at knowledge representation in biology have focused on either ontology development or pathway representation. While the value of both is unquestionable, neither fully supports the data and information integration needs of infectious disease research. We propose an ontology-based approach to pathway representation that extends ontologies beyond single taxonomies and pathway representations to all levels of granularity, thereby allowing the representation of complex biological systems. Our approach builds upon existing ontologies and pathway representations but is grounded in formal ontological and logical principles. Our overall goal is to test empirically the degree to which the ontology-based representation can improve data interpretation and analysis for translational medicine. We will take as our case study Staphylococcus aureus infection, utilizing the invaluable data resources of the Duke Staphylococcus aureus Bacteremia Group. We will achieve our goal through the following three specific aims: 1. Create an ontology-based representation of host-pathogen interactions, focusing on Staphylococcus aureus bacteremia. 2. Empirically test the ability of the ontology-based representation created in Aim 1 to improve data analysis and interpretation by using the representation to predict disease genes associated with Staphylococcus aureus bacteremia. 3. Empirically test the impact of the ontology-based representation created in Aim 1 on understanding of Staphylococcus aureus pathogenesis, on identification of novel therapeutic targets, and on improvement to patient management by testing experimentally the disease gene predictions made under Aim 2. The anticipated outcomes are: an ontology-based method for the representation of complex biological systems and an ontology of host-pathogen interactions, both subjected to tests designed to demonstrate their utility to clinical and translational research; an improved understanding of the immune response to bacterial pathogens; and the identification of genes associated with Staphylococcus aureus bacteremia that can be used to develop novel diagnostics and therapeutics.The resources developed under this proposal will directly improve data integration, retrieval and analysis, will support cross-disciplinary collaborations within infectious disease research, and will provide a foundation from which to develop similar resources for other areas in biomedicine, thus significantly impacting biomedical research and translational medicine.

描述（由申请人提供）：生物医学研究的进展及其转化为临床实践需要整合多个尺度（分子、细胞、生物体）、生物体类型和研究领域的数据。在传染病研究中，数据集成的需求尤其迫切，因为生物体在所有尺度上相互作用，而这些相互作用导致了特定于这些相互作用的过程和结构的出现。真正的数据集成，即联合解释和分析异质类型数据的能力，取决于将数据与数据所涉及的生物实体的信息联系起来的能力。面对快速增长的数据和信息量，从数据到信息的这种链接必须是可计算的。数据和信息之间链接的自动处理要求使用通用的、形式化的知识表示系统来表达它们。生物学中知识表示的努力主要集中在本体开发或路径表示上。虽然两者的价值是毋庸置疑的，但两者都不能完全支持传染病研究的数据和信息集成需求。我们提出了一种基于本体论的路径表示方法，将本体论从单一分类法和路径表示扩展到所有粒度级别，从而允许复杂生物系统的表示。我们的方法建立在现有的本体论和路径表示的基础上，但以正式的本体论和逻辑原则为基础。我们的总体目标是通过实证检验基于本体的表示可以在多大程度上改善转化医学的数据解释和分析。我们将以金黄色葡萄球菌感染作为案例研究，利用杜克金黄色葡萄球菌菌血症组的宝贵数据资源。我们将通过以下三个具体目标来实现我们的目标： 1. 创建基于本体论的宿主-病原体相互作用的表示，重点关注金黄色葡萄球菌菌血症。 2. 通过使用表征来预测与金黄色葡萄球菌菌血症相关的疾病基因，经验测试目标 1 中创建的基于本体的表征的能力，以改进数据分析和解释。 3. 通过实验测试目标 2 下做出的疾病基因预测，实证测试目标 1 中创建的基于本体的表示对理解金黄色葡萄球菌发病机制、识别新治疗靶点以及改善患者管理的影响。结果是：一种基于本体论的复杂生物系统表示方法和宿主-病原体相互作用的本体论，两者都经过旨在证明其对临床和转化研究的实用性的测试；更好地了解对细菌病原体的免疫反应；以及鉴定与金黄色葡萄球菌菌血症相关的基因，可用于开发新的诊断和治疗方法。根据该提案开发的资源将直接改进数据集成、检索和分析，支持传染病研究中的跨学科合作，并将为生物医学其他领域开发类似资源奠定了基础，从而对生物医学研究和转化医学产生重大影响。

项目成果

Design and evaluation of a bacterial clinical infectious diseases ontology.

细菌临床传染病本体的设计和评估。

• 发表时间: 2013
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Gordon,ClaireL;Pouch,Stephanie;Cowell,LindsayG;Boland,MaryRegina;Platt,HeatherL;Goldfain,Albert;Weng,Chunhua
• 通讯作者: Weng,Chunhua

owlcpp: a C++ library for working with OWL ontologies.

owlcpp：用于使用 OWL 本体的 C 库。

• DOI: 10.1186/s13326-015-0035-z
• 发表时间: 2015
• 期刊: Journal of biomedical semantics
• 影响因子: 1.9
• 作者: Levin,MikhailK;Cowell,LindsayG
• 通讯作者: Cowell,LindsayG

Logical development of the cell ontology.

• DOI: 10.1186/1471-2105-12-6
• 发表时间: 2011-01-05
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Meehan TF;Masci AM;Abdulla A;Cowell LG;Blake JA;Mungall CJ;Diehl AD
• 通讯作者: Diehl AD