Improved Pattern Recognition for Functional Genomics

改进功能基因组学的模式识别

• 批准号: 6765680
• 负责人: Ka Yee Yeung-Rhee
• 金额: $ 14.15万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6765680
• 关键词: computer program /software; computer system design /evaluation; data management; functional /structural genomics; genetic markers; hepatitis C virus; hepatocellular carcinoma; high throughput technology; human data; liver disorder; mathematical model; microarray technology; model design /development; molecular biology information system; statistics /biometry

DESCRIPTION (provided by applicant): Computational methods have become intrinsic to biomedical research. The overall goal is to provide Dr. Ka Yee Yeung (Ph.D. in Computer Science) with mentored training and research experience to transition into an independent multi-disciplinary investigator in biomedical research. A program of mentored research, academic coursework, and research plan has been designed for this purpose. The mentored research component consists of mentors and an advisory committee who are leading experts in molecular biology, proteomics, medical research, bioinformatics and statistics. The academic coursework component will provide Dr. Yeung with a solid background in molecular biology, cancer biology and statistics. The underlying theme of the research plan is development of methods and software tools to facilitate extraction of biological meanings from high throughput data in cancer and disease investigation. The major goals of our research plan are the following: Specific Aim 1: Development of improved algorithms for class prediction and identification of gene markers on microarray data related to Hepatocellular carcinoma (HCC) and Hepatitis C virus (HCV) associated liver disease. The problems of predicting the diagnostic or prognostic category of a given tissue sample (class prediction) and identifying potential gene markers from microarray data have received a lot of attention. We will develop improved algorithms for class prediction and identification of potential gene markers by taking advantage of variability over repeated measurements in microarray data. Specific Aim 2: Development of class prediction and class discovery algorithms on heterogeneous data. We will build on our previous work in cluster analysis and class prediction to develop algorithms to handle data from multiple sources, including microarray data, proteomics data and clinical data. Specific Aim 3: Development of improved visualization tools. Software tools for visualization will be developed to facilitate biologists to utilize their biological knowledge and to interpret computational results from high throughput data. Specific Aim 4: Development of practical guidelines for cluster analysis on microarray data. We will make use of our in-house database consisting of thousands of microarray experiments to conduct empirical studies to develop practical guidelines for cluster analysis.

描述（由申请人提供）：计算方法已经成为生物医学研究的本质。总体目标是为杨家仪博士（计算机科学博士）提供指导培训和研究经验，使其成为生物医学研究领域的独立多学科研究者。为此目的设计了一个指导研究、学术课程和研究计划的项目。受指导的研究部分由导师和一个咨询委员会组成，他们是分子生物学、蛋白质组学、医学研究、生物信息学和统计学方面的主要专家。学术课程将为杨博士提供扎实的分子生物学、癌症生物学和统计学背景。该研究计划的基本主题是开发方法和软件工具，以促进从癌症和疾病调查的高通量数据中提取生物学意义。