RF-SRC: A Unified Data Tool

RF-SRC：统一数据工具

• 批准号: 8676476
• 负责人: Hemant Ishwaran
• 金额: $ 24.14万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-10 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8676476
• 关键词: Address; Algorithms; Architecture; Area; Biological; Cancer Patient; Cell Line; Cell Proliferation; Characteristics; Charge; Classification; Clinical; Code; Collaborations; Collection; Communities; Complement; Computer software; Coupled; DNA Damage; Data; Data Analyses; Data Compression; Databases; Decision Making; Diagnostic Neoplasm Staging; Effectiveness; Environment; Gene Expression Profiling; Genes; Genetic; Genomics; Glioblastoma; Health Professional; Human; Hybrids; Interferons; Internet; Java; Learning; Libraries; Licensing; Malignant Neoplasms; Malignant neoplasm of esophagus; Measures; Methodology; Methods; Mining; Modeling; Molecular; Neoplasm Metastasis; Oncogenes; Oncologist; Operative Surgical Procedures; Outcome; Pathologic; Pathway interactions; Patients; Performance; Physicians; Postoperative Period; Primary Neoplasm; Prior Therapy; Procedures; Process; Proliferation Marker; Proteins; Public Domains; Radiation; Recommendation; Resistance; Sampling; Scheme; Solutions; Stage Grouping; Staging; Staging System; The Cancer Genome Atlas; Therapeutic; Trees; Weight; Work; base; cancer type; chemotherapy; forest; gene interaction; graphical user interface; improved; malignant breast neoplasm; neoplastic cell; novel; preference; raf Kinases; statistics; tool; tumor growth; user friendly software; user-friendly

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.

描述（由申请人提供）：集成学习涉及采用基本程序（基础学习器）并将它们组合起来形成集成的简单任务。这个简单的过程通常会产生具有卓越性能的预测器；最成功的例子之一是随机森林（RF），这是一种使用随机树基学习器形成的集合。在这个项目中，我们使用射频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大部分工作都集中在基因表达谱上。这些特征主要是增殖标记，并不能清楚地识别新的或转移特异性的途径。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 途径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。在详细研究出 RKIP 通路后，该项目将使用 RF 从原发肿瘤样本的表达数据中统计验证 RKIP 可操作驱动临床转移。然而，这造成了一个两难的境地。虽然森林是拟合相互作用的理想工具，但目前不存在严格的方法来理清森林内高度复杂的变量关系，也没有全面且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，我们引入了一种新的变量选择统计数据来识别相互作用，并用它来验证 RKIP 路径。我们开发了一个统一的框架来促进该统计数据的总体使用。在另一个应用中，我们引入了用于构建基因通路的分组变量比较。利用这一点，我们扩展了干扰素相关 DNA 损伤抵抗特征 (IRDS) 的工作，这是一种治疗特征，可以预测多种常见人类癌症对化疗和/或放射的抵抗力。我们描述了基于多维基因组数据的 IRDS 监管生物网络。该网络的边缘使用变量相关性的 RF 测量进行加权，以查明重要的基因间相互作用。在另一个主要方向中，我们使用独特的丰富的全球食管癌数据库，使用新颖的 RF 算法进行分期和预测，为食管癌患者描述个性化治疗建议。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一种新的强大数据分析工具，用于个性化预测和治疗决策。为了分享该项目带来的 RF 方法和统计方面的进步，我们开发了一个用户友好的统一 RF 软件 RF-SRC，该软件将根据 GNU 公共许可证免费提供。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。