Enabling new translational discoveries using a genomic data-driven nosology

使用基因组数据驱动的疾病分类学实现新的转化发现

• 批准号: 8451401
• 负责人: ATUL J BUTTE
• 金额: $ 59.76万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8451401
• 关键词: Address; Age; American; Automobile Driving; Biological; Biological Markers; Cancer Immunology Science; Classification; Clinic; Clinical; Clinical Medicine; Clinical Trials; Collaborations; Complex; Daphne plant; Data; Databases; Development; Diagnostic tests; Disease; Disease model; Drug Targeting; Epidemiology; Funding; Gene Expression; Genetic; Genomics; Government; Imagery; Immune System Diseases; Informatics; Insurance; International Classification of Diseases; Journals; Knowledge; Life; Link; Malignant neoplasm of lung; Measurement; Medicine; Mercury; Messenger RNA; Methods; Modeling; Molecular; New York; Nomenclature; Ontology; Pathologist; Pathology; Pathway interactions; Pharmaceutical Preparations; Pharmacogenomics; Physicians; Proteins; Proteomics; Publications; Quantitative Trait Loci; Rare Diseases; Research; Research Personnel; Science; Serum; Signal Transduction; Structure; System; Taxonomy; Techniques; Testing; Therapeutic; Time; Tissues; Vocabulary; Work; World Health Organization; base; disease classification; health organization; human disease; knowledge base; lung small cell carcinoma; mouse model; news; novel; novel diagnostics; novel therapeutics; repository; research study; symposium; therapeutic target; tool

DESCRIPTION (provided by applicant): Many forms of biomolecular (e.g., gene expression, genetics, proteomics) and clinical (e.g., clinical biomarkers, drug targets and indications) data pertaining to many different diseases are now readily available from publicly- available data repositories and knowledge-bases. There is now an opportunity to integrate these data into a unified, globally coherent representation of human disease, or nosology. Such a nosology would express how diseases are related to one another across multiple molecular and clinical axes. In this competitive renewal, we are planning a major expansion for this project. We plan to capture data from newer public repositories with more types of molecular measurements. Inclusion of genetic and protein measurements will enable a richer modeling of diseases and disease similarity, beyond mRNA measurements. To help link the molecular changes seen in disease to genetic differences, we plan to incorporate Expression Quantitative Trait Loci (eQTLs) into our disease models, built from simultaneous genetic and expression measurements. To expand the utility of our nosology in personalized medicine, we plan to incorporate more quantitative epidemiological measurements on disease, and to model transitions between disease states using probabilistic relational modeling. We will compare our nosology with the well-known ICD-10 as well as ICD-11, under development. We will develop novel visualization methods for the complex of edges and nodes seen in nosologies. We also plan to test our nosology in two Driving Biological Projects, in small cell lung cancer and immunology and disease, specifically yielding novel diagnostics and therapeutics ready for clinical trials.

描述（由申请人提供）：与许多不同疾病相关的许多形式的生物分子（例如，基因表达，遗传学，蛋白质组学）和临床（例如，临床生物标志物，药物靶点和适应症）数据现在可以从公开可用的数据库和知识库中随时获得。现在有机会将这些数据整合到一个统一的、全球连贯的人类疾病或疾病分类学中。这样的疾病分类学将表达疾病如何在多个分子和临床轴上相互关联。在这个竞争激烈的更新中，我们计划对这个项目进行一次大规模的扩建。我们计划从更新的公共存储库中获取更多类型的分子测量数据。包含遗传和蛋白质测量将使疾病和疾病相似性的建模更加丰富，超越mRNA测量。为了帮助将疾病中的分子变化与遗传差异联系起来，我们计划将表达数量性状位点（eQTLs）结合到我们的疾病模型中，该模型是通过同时进行遗传和表达测量建立的。为了扩大我们的疾病分类学在个性化医疗中的应用，我们计划在疾病中纳入更多的定量流行病学测量，并使用概率关系建模来模拟疾病状态之间的转换。我们将把我们的分类学与著名的ICD-10和正在开发中的ICD-11进行比较。我们将开发新的可视化方法，用于在分类学中看到的复杂的边和节点。我们还计划在两个驱动生物学项目中测试我们的分类学，分别是小细胞肺癌和免疫学和疾病，特别是为临床试验提供新的诊断和治疗方法。