Prediction and Network Construction Using High-throughput Data

描述(申请人提供)：生物标志物鉴定正在成为像微阵列和质谱学这样的高通量技术的重要用途。这些高通量数据(特别是微阵列数据)被广泛用于组织类型分类，包括各种肿瘤类型、患者生存时间预测、复发时间和其他临床相关的时间量。这些高通量数据衡量了数千个潜在预测因子的活性水平(在基因表达数据的情况下是基因，在质谱仪或蛋白质微阵列数据的情况下是肽)。这些数据的分析带来了困难的统计问题，因为测量的特征的数量远远大于通常可用的组织样本的数量。此外，许多不同的预报器集合产生类似的预测精度。在这里，我们建议将生物学知识结合到一个监督框架中，以确定对生物学有意义的预测因素，用于分类和生存分析。为此，我们将开发贝叶斯模型平均(BMA)方法，以产生简单、可靠、稳健和可解释的预测。BMA还提供了一种概率多元特征选择方法。作为这项工作的一部分，我们将扩展最近开发的社交网络潜在位置集群模型，以推断生物网络和识别网络模块。网络属性(例如，模块和连接度)赋予生物学意义。因此，我们将在监督框架中集成网络属性，以识别具有生物意义的预测因子。我们将扩展BMA方法以确定预测网络模块和预定义的基因类别(例如GO类别、KEGG通路)。这一建议有两个主要的计算任务：(1)发展用于多类分类和生存分析的BMA方法(目标1)；(2)发展用于推断生物网络和识别网络模块的潜在位置聚类模型(目标3)。这两个计算任务在目标2中是统一的，其中我们使用了监督BMA框架中的网络模块和属性。在目标4中，我们将生成表达式扰动数据来评估我们的网络构造方法。最后，我们将使软件和生成的数据公开。该方案中开发的方法一般适用于许多高吞吐量数据类型。然而，由于我们将生成表达扰动数据来验证和完善所构建的表达网络，因此我们将重点将我们开发的方法应用于基因表达数据。与公共健康相关：生物标记物鉴定正在成为像微阵列这样的高通量技术的重要用途。该建议旨在为组织类型分类确定具有生物学意义的预测性生物标记物，包括各种肿瘤类型、患者生存时间预测、复发时间和其他临床相关的时间量。该项目可能会带来廉价、准确和强大的诊断测试，从而提高癌症或其他疾病患者诊断或预后的准确性。

DESCRIPTION (provided by applicant): Biomarker identification is becoming an important use for high-throughput technologies like microarrays and mass spectrometry. These high-throughput data (especially microarray data) are used extensively for tissue type classification, including various tumor types, patient survival time prediction, time to relapse, and other clinically relevant temporal quantities. These high-throughput data measure the activity levels of thousands of potential predictors (genes in the case of gene expression data and peptides in the case of mass spectrometry or protein microarray data). The analysis of these data poses difficult statistical problems since the number of features measured is far larger than the number of tissue samples that are typically available. Moreover, many different sets of predictors produce similar prediction accuracies. Here, we propose to incorporate biological knowledge into a supervised framework to identify biologically meaningful predictors for classification and survival analysis. Towards this end, we will develop Bayesian Model Averaging (BMA) methods to produce simple, reliable, robust, and interpretable predictions. BMA also provides a probabilistic multivariate feature selection method. As part of this effort, we will extend the recently developed latent position cluster model for social networks to infer biological networks and identify network modules. Network properties (e.g., modules and the degree of connectivities) confer biological meanings. Hence, we will integrate network properties in a supervised framework to identify biologically meaningful predictors. We will extend the BMA methods to determine predictive network modules and pre-defined gene categories (e.g. GO categories, KEGG pathways). This proposal has two main computational thrusts: (1) the development of BMA methods for multi-class classification and survival analysis (Aim 1); and (2) the development of latent position cluster model for inferring biological networks and identifying network modules (Aim 3). These two computational thrusts are unified in Aim 2 in which we use network modules and properties in the supervised BMA framework. In Aim 4, we will generate expression perturbation data to evaluate our network construction methods. Finally, we will make the software and data generated publicly available. The methods developed in this proposal are generally applicable to many high-throughput data types. However, since we will generate expression perturbation data to validate and refine the constructed expression networks, we will focus on applying our developed methods to gene expression data. PUBLIC HEALTH RELEVANCE: Biomarker identification is becoming an important use for high-throughput technologies like microarrays. This proposal aims to identify biologically meaningful predictive biomarkers for tissue type classification, including various tumor types, patient survival time prediction, time to relapse, and other clinically relevant temporal quantities. This project could lead to inexpensive, accurate and robust diagnostic tests that increase the accuracy of diagnoses or prognoses for patients with cancer or other diseases.