Statistical Methods for Predicting Survival Outcomes from Genomic Data

从基因组数据预测生存结果的统计方法

• 批准号: 7138117
• 负责人: ANNETTE M MOLINARO
• 金额: $ 15.92万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-06 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7138117
• 关键词: cancer prevention; carcinogenesis; mathematical model; motivation; neoplasm /cancer; proteins; statistics /biometry; university

DESCRIPTION (provided by applicant): Dr. Annette Molinaro is an Assistant Professor in the Division of Biostatistics in the Department of Epidemiology and Public Health at Yale University School of Medicine. Prior to arriving at Yale, Dr. Molinaro was a Cancer Prevention Fellow at the National Cancer Institute. Her long term career goal is to develop statistical and computational methods which elucidate mechanisms of cancer pathogenesis to be used for the purposes of cancer prevention, diagnosis, and treatment. To reach this goal she has outlined two areas which are in need of enhancement: 1) her knowledge of functional genomics specifically related to carcinogenesis; and, 2) her proficiency in computer programming for the purposes of searching for and extracting pertinent information from vast data structures. A comprehensive understanding of the biological mechanisms behind carcinogenesis as well as the advanced computational skills necessary to implement novel statistical methods will propel Dr. Molinaro's independent research program. To meet these needs, Dr. Molinaro will: 1) attend classes at Yale University in genomics, bioinformatics, computer science, and molecular biology; 2) participate in world renowned courses at the Jackson and Cold Spring Harbor Laboratories in mammalian genetics, computational and comparative genomics, and complex trait analysis; and, 3) attend scientific meetings and workshops to present her K22 research, build collaborations, and engage in scientific discussion on current issues concerning statistical genomics. Her proposed research project entails a comprehensive, aggressive search of genomic, epidemiologic, and histologic data for the purposes of predicting a clinical outcome of interest, such as time to recurrence or death. Dr. Molinaro has established a univariate approach to this problem; however, she now needs to expand this to a realistic biological setting. The primary aims of this research project are: 1) to account for missing values in the genomic variables; 2) evaluate measures of variable importance; and, 3) extend this approach to encompass other statistical models such as wavelets and splines. This K22 grant will enable Dr. Molinaro the protected time and resources to accomplish her training in the molecular biology of cancer, establish collaborations at Yale University and beyond, and provide the scientific community with a much needed tool for associating genomic data with clinical outcomes. Relevance: Dr. Molinaro's research incorporates genomic, histological, and epidemiological information in order to predict a clinical outcome, such as time to disease progression. It is methods such as this that will provide greater clarity within the complexity of carcinogenesis and allow for more targeted methods of cancer prevention and control.

描述(由申请人提供)：Annette Molinaro博士是耶鲁大学医学院流行病学和公共卫生系生物统计学系的助理教授。在来到耶鲁之前，莫利纳罗博士是美国国家癌症研究所的癌症预防研究员。她的长期职业目标是开发统计和计算方法，阐明癌症发病机制，用于癌症预防、诊断和治疗。为了实现这一目标，她概述了两个需要加强的领域：1)她对特定与癌症发生有关的功能基因组学的知识；2)她精通计算机编程，以便从海量数据结构中搜索和提取相关信息。全面了解癌症发生背后的生物学机制，以及实施新的统计方法所需的高级计算技能，将推动Molinaro博士的独立研究计划。为了满足这些需求，Molinaro博士将：1)参加耶鲁大学基因组学、生物信息学、计算机科学和分子生物学的课程；2)参加杰克逊实验室和冷泉港实验室关于哺乳动物遗传学、计算和比较基因组学以及复杂性状分析的世界知名课程；3)参加科学会议和研讨会，介绍她的K22研究，建立合作，并参与与统计基因组学相关的当前问题的科学讨论。她提出的研究项目需要对基因组、流行病学和组织学数据进行全面、积极的搜索，以预测感兴趣的临床结果，如复发或死亡的时间。莫利纳罗博士已经建立了一个单变量方法来解决这个问题；然而，她现在需要将其扩展到现实的生物学环境中。这个研究项目的主要目的是：1)解释基因组变量中的缺失值；2)评估变量重要性的度量；以及3)将这种方法扩展到包括其他统计模型，如小波和样条线。这笔K22拨款将使Molinaro博士有足够的时间和资源完成她在癌症分子生物学方面的培训，在耶鲁大学和其他地方建立合作关系，并为科学界提供将基因组数据与临床结果联系起来的迫切需要的工具。相关性：Molinaro博士的研究结合了基因组、组织学和流行病学信息，以预测临床结果，如疾病进展时间。正是这样的方法将在癌症发生的复杂性中提供更大的清晰度，并允许更有针对性的癌症预防和控制方法。