CompBio: Collaborative Research: Development of Effective Gene Selection Algorithms for Microarray Data Analysis

CompBio：合作研究：开发用于微阵列数据分析的有效基因选择算法

• 批准号: 0621889
• 负责人: Haesun Park
• 金额: $ 25万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621889&HistoricalAwards=false
• 关键词: CompBio; Collaborative; Research; Development; Effective

With the success of the Human Genome Project, a microarray can now potentially handle the genes in an entire genome scale. A typical microarray data set involves a massive number of genes. A dramatic dimension reduction to a much smaller number of significant genes, responsible for specific conditions, can potentially increase the possibility of further biological study and knowledge regarding the roles of specific genes.Any methodology that can improve our recognition of significant genes among a large number of genes, and often a limited set of available experimental results, could have a significant impact on our understanding of diseased and normal states, and eventually on diagnosis, prognosis, and drug design. The method that we propose to investigate here is intended to provide critical information on the roles of genes where the key component of our approach is subspace-based methods, which have demonstrated great success in numerous pattern recognition tasks including efficient classification, clustering, and fast search.The development of effective computer-based algorithms for gene selection is indispensable since it is virtually impossible to rely solely on biological testing due to the enormous complexity of the problems. What is novel and unique in our proposed research is that we seek to find a mathematically rigorous framework that models gene selection problems, with careful consideration of the significance of the biological characteristics of the problem. Utilizing our knowledge and previous results on feature extraction, and by discovering their mathematical relationship to feature selection, efficient and effective nonparametric methods for gene selection will be designed. An important role will be played by the nonnegative matrix factorization in building a mathematically rigorous bridge between feature extraction and feature selection in our proposed research. In the process, we will also explore novel methods for estimating missing values as a preprocessing stage of gene selection based on the alternating least squares and the structured total least norm formulations. All results obtained, the new algorithms and software developed, as well as the new data sets generated and compiled will be made available to the research community, to teaching faculty, and to both graduate and undergraduate students, using existing Web servers at the Georgia Institute of Technology and University of Texas at Dallas.Intellectual Merit: This research will produce methods that will have a great impact on computational microarray analysis. The gene selection and missing value estimation methods developed in this research allow significant reduction in complexity of biological testing due to the initial reduction of the problem dimension, thus substantially improve detailed study of significant genes. The feature selection and feature extraction algorithms developed in this research will be applicable to many other problems where data sets in high dimensional spaces need to be handled efficiently and effectively, such as text processing, facial recognition, finger print classification, iris recognition. The missing value estimation methods designed in this research can also be utilized in recovering missing data such as in collaborative filtering.Broader Impact: The research will enhance advanced theory of computational biology and bioinformatics. The developed techniques will also have potential applications in database management, medical examination and diagnosis, bio-chemical selection, and biological networks. The graduate student involvement in this research will have numerous future benefits. The discovery and research experience of the students will prepare them for productive careers in academia, research labs, and industry in highly important current research areas in bioinformatics.

随着人类基因组项目的成功，微阵列现在可以在整个基因组量表中处理基因。典型的微阵列数据集涉及大量基因。较小数量的重要基因（负责特定疾病）可能会增加进一步的生物学研究的可能性和有关特定基因作用的知识的可能性。任何可以提高我们对大量基因中重要基因的认识的方法，通常会对我们对疾病的状态和正常状态和正常状态的认识和诊断的认识产生重大影响。我们建议在此处进行调查的方法旨在提供有关基因作用的关键信息，在该基因的作用中，我们方法的关键组成部分是基于子空间的方法，在许多模式识别任务中已经取得了巨大的成功，包括有效的基于计算机的算法的基因算法的发展是无关紧要的，因为在基于计算机的算法是无关紧要的，因为它实际上是不可或缺的，因此基于计算机的算法是无关紧要的。在我们提出的研究中，新颖和独特的是，我们试图找到一个在数学上进行严格的框架，以模拟基因选择问题，并仔细考虑了问题的生物学特征的重要性。通过利用我们的知识和先前的特征提取结果结果，并将设计其数学关系与特征选择，有效有效的非参数方法，用于基因选择。在我们提出的研究中，非负矩阵分解将在数学上严格的桥梁之间构建数学上严格的桥梁中发挥重要作用。在此过程中，我们还将探讨基于交替的最小二乘和结构化总数最小规范公式的基因选择的预处理阶段，以估算缺失值的新方法。获得和编译的新算法和软件以及新的数据集将提供给研究社区，教师教学和研究生和本科生，使用佐治亚州技术学院和德克萨斯大学的佐治亚州大学的现有网络服务器，将对dallas.intlectual serit进行分析，该方法将对计算产生一定的影响。这项研究中开发的基因选择和缺失的价值估计方法可显着降低生物测试的复杂性，这是由于问题维度的初始降低，因此实质上改善了对重要基因的详细研究。在本研究中开发的功能选择和特征提取算法将适用于许多其他问题，在许多其他问题中，需要高维空间中的数据集需要有效，有效地处理，例如文本处理，面部识别，手指打印分类，IRIS识别。本研究中设计的缺失价值估计方法也可以用于恢复缺失的数据，例如在协作过滤中。Boader的影响：研究将增强计算生物学和生物信息学的高级理论。开发的技术还将在数据库管理，体检和诊断，生物化学选择和生物网络中具有潜在的应用。研究生参与这项研究将有许多未来的好处。学生的发现和研究经验将为他们在生物信息学目前非常重要的研究领域的学术界，研究实验室和行业的生产力职业做好准备。