RF-SRC: A Unified Data Tool

描述（由申请人提供）：包围学习涉及采取基本程序（基础学习者）和组合它们形成一个合奏的简单任务。这个简单的过程通常会产生具有上级性能的预测器;最成功的例子之一是随机森林（RF），一种使用随机树基学习器形成的集成。在这个项目中，我们使用RF来研究一系列与癌症相关的问题。一个重点领域涉及乳腺癌的特定途径。迄今为止，阐明乳腺癌分子特征的许多工作都集中在基因表达谱上。这些特征主要是增殖的标志物，不能清楚地识别新的或转移特异性途径。我们最近通过实验展示了乳腺癌基因Raf激酶抑制蛋白（RKIP）如何调节特定的转移途径。重要的是，RKIP通路不影响原发性肿瘤生长或细胞增殖，而是涉及转移特异性步骤。在实验详细研究了RKIP通路之后，本项目将使用来自原发性肿瘤样本的表达数据，使用RF来统计学验证RKIP可操作地驱动临床转移。然而，这造成了一个两难局面。虽然森林是拟合相互作用的理想工具，但目前还没有严格的方法来解开森林中高度复杂的变量关系，也没有全面和严格的方法来选择变量。在这个项目中，我们开发了一个统一的预测和变量选择框架来解决这个问题。应用这一点，我们引入了一个新的变量选择统计识别的相互作用，并使用它来验证RKIP途径。我们制定了一个统一的框架，以方便一般使用这一统计数据。在另一个应用中，我们引入分组变量比较来构建基因通路。利用这一点，我们扩展了我们在干扰素相关的DNA损伤抗性特征（IRDS）上的工作，IRDS是一种治疗特征，可以预测各种常见人类癌症对化疗和/或放疗的抗性。我们描述了一个基于多维基因组学数据的IRDS调控生物网络。该网络的边缘使用RF变量相关性度量来加权，以确定重要的基因-基因相互作用。在另一个主要目标中，使用独特丰富的全球食管癌数据库，我们使用一种用于分期分组和分期的新型RF算法描述了食管癌患者的个性化治疗建议。该算法足够通用，可以应用于其他癌症，从而为医生、肿瘤学家和其他癌症医疗保健专业人员提供了一种新的强大的数据分析工具，用于个性化诊断和治疗决策。为了分享这个项目所带来的RF的方法和统计进步，我们开发了一个用户友好的统一RF软件RF-SRC，在GNU公共许可证下免费提供。该软件将通过利用最先进的并行化解决方案来实现大规模的可扩展性。

DESCRIPTION (provided by applicant): Ensemble learning involves the simple task of taking elementary procedures (base learners) and combining them to form an ensemble. This simple process often yields a predictor with superior performance; one of the most successful examples is random forests (RF), an ensemble formed using random tree base-learners. In this project we use RF to study a collection of cancer related problems. One area of focus involves a specific pathway in breast cancer. To date much of the work in elucidating the molecular characteristics of breast cancer has focused on gene expression profiling. These signatures are principally markers for proliferation and do not clearly identify novel or metastasis-specific pathways. We recently experimentally showed how the breast cancer gene Raf Kinase Inhibitory Protein (RKIP) regulates a specific metastasis pathway. Importantly, the RKIP pathway does not influence primary tumor growth or cell proliferation but rather involves metastasis-specific steps. Having worked out the RKIP pathway in experimental detail, this project will use RF to verify statistically that RKIP operationally drives clinical metastasis usin expression data from primary tumor samples. However, this poses a dilemma. While forests are ideal tools for fitting interactions, no rigorous methodology currently exists for untangling the highly involved variable relationships within a forest and there is no comprehensive and rigorous method for selecting variables. In this project we develop a unified prediction and variable selection framework to address this. Applying this we introduce a new variable selection statistic for identifying interactions and use this to validate the RKIP pathway. We develop a unified framework to facilitate the use of this statistic in general. In another application, we introduce grouped variable comparisons for building gene-pathways. Using this we expand our work on the Interferon-Related DNA Damage Resistance Signature (IRDS), a therapeutic signature that can predict resistance to chemotherapy and/or radiation across a wide variety of common human cancers. We describe a regulatory biological network for the IRDS based on multi-dimensional genomics data. Edges of this network are weighted using a RF measure of variable-relatedness to pin-point important gene-gene interactions. In another major thrust, using a uniquely rich worldwide esophageal cancer database, we describe individualized treatment recommendations for esophageal cancer patients using a novel RF algorithm for stage- grouping and prognostication. The algorithm is general enough that it can be applied to other cancers, thus providing physicians, oncologists, and other cancer health care professionals with a new powerful data-analytic tool for individualized prognostication and treatment decision making. To share the methodological and statistical advancements of RF arising from this project we develop a user friendly unified RF software, RF-SRC, to be made freely available under the GNU Public License. This software will allow for massive scalability by utilizing cutting edge parallelization solutions.