New Methods for Cancer Class Discovery and Prediction: Integration, visualization

癌症类别发现和预测的新方法：整合、可视化

基本信息

批准号：
7686950
负责人：
CHARLES DELISI
金额：
$ 20.31万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-12 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7686950
关键词：
Accounting Algorithms Atlases Biological Biological Markers Biological Process Biology Characteristics Classification Clinical Communication Communities Computer software Computing Methodologies DNA Data Data Set Databases Development Diagnosis Diagnostic Genes Genetic Genetic Transcription Genomics Goals Heating Imagery Link Malignant Neoplasms Maps Messenger RNA Methods Mining Modification Molecular Operative Surgical Procedures Outcome Pathway interactions Play Prevention Procedures Process Proteins Research Research Personnel Sampling Sensitivity and Specificity Staging Statistical Methods System Testing Variant Visual base cancer genome cancer initiation cancer therapy epigenomics forest improved innovation insight meetings novel prognostic protein protein interaction therapeutic target tool tumor tumor progression vector virtual

项目摘要

DESCRIPTION (provided by applicant): This proposal explores new computational methods for integrating, analyzing and visualizing the rapidly growing genomic and epigenomic information in The Cancer Genome Atlas (TCGA). In the long range these methods and their variants will enable rigorous identification of molecular biomarkers for distinguishing cancer, their subtypes, theirs stages and their outcome, providing the basis for developing improved diagnostics and prognostics. They will also enable identification of the pathways and processes that are central to the initiation and progression of tumors, and thereby inform the choice of therapeutic target selection. Until now most methods for discovering class differences related to cancer have been based on the analysis of mRNA transcription. Here we explore the modification, use and adaptation of advanced statistical methods for integrating TCGA data, and the use of our VISANT mining tool for integrating TCGA with other publicly available data. The long term objective is to develop methods that will be widely disseminated and used to discover reliable biomarkers for cancer development and progression, and to gain a deeper understanding of the key alterations that occur during transformation.

描述（由申请人提供）：该建议探讨了用于整合，分析和可视化癌症基因组图集（TCGA）快速增长的基因组和表观基因组信息的新计算方法。在远距离的情况下，这些方法及其变体将使分子生物标志物进行严格识别，以区分癌症，亚型，它们的阶段和结果，从而为发展改善的诊断和预后提供了基础。他们还将识别肿瘤起始和进展至关重要的途径和过程，从而为治疗靶标选择的选择提供了信息。到目前为止，发现与癌症相关的类别差异的大多数方法都是基于mRNA转录的分析。在这里，我们探讨了用于集成TCGA数据的高级统计方法的修改，使用和适应，以及使用我们的Visant采矿工具将TCGA与其他公共可用数据集成在一起。长期目标是开发将被广泛传播的方法，并用于发现可靠的生物标志物来用于癌症发展和进展，并对转化过程中发生的关键变化有了更深入的了解。