Core--Data Management and Computation Modeling

该综合癌症生物学计划（ICBP）的目标是：1）增加我们对肿瘤中复杂表观遗传改变的理解; 2）利用这些高端信息改善女性癌症的预后、干预和治疗。为了帮助实现这些目标，核心B研究者将：具体目标1：为维护和管理交互式数据库提供最先进的计算和统计支持。利用Java（TM）技术，我们开发了基因组数据可视化工具包（GDVTK），该工具包由一组数据结构和核心类组成。这个GDVTK是一个良好的框架，用于开发基于Web的应用程序，以可视化形式呈现基因组注释。我们会委聘 GDVTK开发一个强大的，灵活的数据管理系统，用于存储和查询启动子CpG岛和相关的甲基化和遗传变化，组蛋白修饰和染色质状态的癌细胞系，肿瘤上皮细胞和肿瘤基质。具体目标2：开发创新的贝叶斯方法来预测表观遗传和遗传变量的结果。监督和非监督分类方法将用于数据挖掘， 表观基因组结果。大多数机器学习方法都是数据密集型的，容易过度拟合，这两种方法在应用于新数据集时都会导致假阳性预测。为了解决这个问题，我们将使用交叉验证和排列测试方法的组合，以产生强大的统计模型。具体目标3：为ICBP项目中产生的微阵列数据的分析和报告提供咨询。例如，我们将提供方法来解决分析大型，复杂的表观基因组数据集所固有的问题。该核心还将ICBP项目的相关数据与其他分布式资源（如GenBank和CaBIG（癌症生物医学信息网格））集成到一个集中的数据仓库中。数据库（http：//bioinformatics.med.ohio-state.edu/ICBP）将通过用户友好的网络界面提供给所有研究者。

The goals of this Integrated Cancer Biology Program (ICBP) are to 1) increase our understanding of complex epigenetic alterations in neoplasms and 2) use this high-end information for improved prognosis, intervention and treatment of female cancers. In order to help accomplish these goals, the Core B investigators will : Specific Aim 1: Provide state-of-the-art computational and statistical support for maintaining and managing an interactive database. Using Java(TM) technology, we have developed Genome Data Visualization Toolkit (GDVTK) that consists of a set of data structures and core classes. This GDVTK is a sound framework for developing web-based applications to present the genomic annotations in visual form. We will employ GDVTK to develop a robust, flexible data management system for storage and query of promoter CpG islands and the associated methylation and genetic changes, histone modifications and chromatin status in cancer cell lines, neoplastic epithelium, and tumor stroma. Specific Aim 2: Develop innovative Bayesian methods to predict outcomes of epigenetic and genetic variables. Both supervised and unsupervised classification methods will be use for data mining of epigenomic results. Most of the machine-learning methods are data-intensive and susceptible to over-fitting, both of which lead to false-positive predictions when applied to new datasets. To address this concern, we will use a combination of cross-validation and permutation testing methods to produce robust statistical models. Specific Aim 3: Provide consultation in the analysis and reporting of microarray data produced in the proposed ICBP projects. For example, we will provide methods to address problems inherent in analyzing large, complex epigenomic data sets. This Core also integrates relevant data from ICBP projects with other distributed resources, such as GenBank and CaBIG (Cancer Biomedical Informatics Grid), into a centralized data warehouse. The database (http://bioinformatics.med.ohio-state.edu/ICBP) will be made available to all the investigators through a user-friendly web-interface.