RF-SRC: A Unified Data Tool

RF-SRC：统一数据工具

• 批准号: 8528520
• 负责人: Hemant Ishwaran
• 金额: $ 22.73万
• 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/8528520
• 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）：合奏学习涉及采用基本程序（基础学习者）并将其组合形成合奏的简单任务。这个简单的过程通常会产生具有出色性能的预测因子。最成功的例子之一是随机森林（RF），这是一种使用随机树碱性学习者形成的合奏。在这个项目中，我们使用RF研究了与癌症相关的问题的集合。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分析上。这些签名主要是增殖的标志物，并且不能清楚地识别新颖或转移特异性的途径。我们最近通过实验表明，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的生长或细胞增殖，而是涉及转移特异性步骤。该项目通过实验细节制定了RKIP途径，将使用RF统计验证RKIP在操作上驱动来自原发性肿瘤样品的临床转移usin表达数据。但是，这构成了困境。尽管森林是适合相互作用的理想工具，但目前尚无严格的方法来解开森林中高度参与的变量关系，并且没有全面而严格的方法来选择变量。在此项目中，我们开发了一个统一的预测和可变选择框架来解决此问题。应用此信息，我们引入了一个新的变量选择统计量来识别交互作用，并使用它来验证RKIP途径。我们开发了一个统一的框架，以促进总体上使用此统计数据。在另一个应用程序中，我们引入了用于构建基因轨道的分组变量比较。使用此功能，我们将工作扩展到干扰素相关的DNA抗损伤特征（IRDS）上，这是一种治疗性特征，可以预测各种普通人类癌症的化学疗法和/或辐射的耐药性。我们根据多维基因组学数据描述了IRDS的调节生物网络。该网络的边缘使用可变相关性的RF度量与PIN点重要的基因 - 基因相互作用加权。在另一个主要的推力中，使用独特的全球食管癌数据库，我们使用新型的RF算法进行分组和预后，描述了食管癌患者的个性化治疗建议。该算法足够一般，可以将其应用于其他癌症，从而为医师，肿瘤学家和其他癌症保健专业人员提供新的强大数据分析工具，以实现个性化预测和治疗决策。为了分享该项目引起的RF的方法论和统计进步，我们开发了一个用户友好的统一RF软件RF-SRC，可根据GNU公共许可自由提供。该软件将通过利用尖端并行化解决方案来实现大规模的可扩展性。