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.

随着人类基因组计划的成功，微阵列现在可以处理整个基因组规模的基因。典型的微阵列数据集涉及大量基因。将负责特定条件的重要基因的数量大幅减少，可能会增加进一步生物学研究和了解特定基因作用的可能性。任何可以提高我们对大量重要基因的识别的方法基因的序列，以及通常有限的一组可用实验结果，可能会对我们对疾病和正常状态的理解产生重大影响，并最终对诊断、预后和药物设计产生重大影响。我们建议在这里研究的方法旨在提供有关基因作用的关键信息，其中我们方法的关键组成部分是基于子空间的方法，该方法在许多模式识别任务中取得了巨大成功，包括高效分类、聚类和快速识别。开发有效的基于计算机的基因选择算法是必不可少的，因为由于问题的巨大复杂性，实际上不可能仅仅依靠生物测试。我们提出的研究的新颖性和独特之处在于，我们寻求找到一个数学上严格的框架来模拟基因选择问题，并仔细考虑问题的生物学特征的重要性。利用我们在特征提取方面的知识和先前的结果，并通过发现它们与特征选择的数学关系，将设计高效且有效的基因选择非参数方法。在我们提出的研究中，非负矩阵分解在特征提取和特征选择之间建立数学上严格的桥梁方面将发挥重要作用。在此过程中，我们还将探索估计缺失值的新方法，作为基于交替最小二乘和结构化总最小范数公式的基因选择的预处理阶段。获得的所有结果、开发的新算法和软件以及生成和编译的新数据集将使用佐治亚研究所现有的网络服务器提供给研究界、教学人员以及研究生和本科生科技大学和德克萨斯大学达拉斯分校。智力优势：这项研究将产生对计算微阵列分析产生巨大影响的方法。本研究开发的基因选择和缺失值估计方法由于问题维度的初始减少而显着降低了生物测试的复杂性，从而大大提高了对重要基因的详细研究。本研究开发的特征选择和特征提取算法将适用于需要高效处理高维空间中的数据集的许多其他问题，例如文本处理、面部识别、指纹分类、虹膜识别。本研究设计的缺失值估计方法也可用于恢复缺失数据，例如协同过滤。更广泛的影响：该研究将增强计算生物学和生物信息学的先进理论。所开发的技术还将在数据库管理、医学检查和诊断、生化选择和生物网络方面具有潜在的应用。研究生参与这项研究将给未来带来许多好处。学生的发现和研究经验将为他们在学术界、研究实验室和工业界在生物信息学当前非常重要的研究领域中从事富有成效的职业做好准备。