Prediction and Network Construction Using High-throughput Data

描述（由申请人提供）：生物标志物鉴定正在成为高通量技术如微阵列和质谱的重要用途。这些高通量数据（尤其是微阵列数据）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。这些高通量数据测量了数千种潜在预测因子（基因表达数据中的基因和质谱或蛋白质微阵列数据中的肽）的活性水平。这些数据的分析提出了困难的统计问题，因为测量的特征数量远远大于通常可用的组织样本数量。此外，许多不同的预测器集合产生相似的预测精度。在这里，我们建议将生物学知识纳入一个监督框架，以确定生物学上有意义的分类和生存分析预测因子。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变量特征选择方法。作为这项工作的一部分，我们将扩展最近开发的社会网络潜在位置聚类模型，以推断生物网络并识别网络模块。网络属性（例如，模块和连接程度）赋予生物学意义。因此，我们将在监督框架中整合网络属性，以识别具有生物学意义的预测因子。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途径）。该建议有两个主要的计算重点：(1)开发用于多类分类和生存分析的BMA方法（目标1）；(2)开发用于推断生物网络和识别网络模块的潜在位置聚类模型（Aim 3）。这两个计算推力在Aim 2中是统一的，我们在监督BMA框架中使用网络模块和属性。在Aim 4中，我们将生成表达式摄动数据来评估我们的网络构建方法。最后，我们将把生成的软件和数据公开。本建议中开发的方法通常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构建的表达网络，我们将专注于将我们开发的方法应用于基因表达数据。公共卫生相关性：生物标志物鉴定正成为微阵列等高通量技术的重要应用。该提案旨在确定具有生物学意义的预测性生物标志物，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。这一项目可能导致廉价、准确和可靠的诊断测试，从而提高癌症或其他疾病患者的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.