Flexible statistical machine learning techniques for cancer-related data

用于癌症相关数据的灵活统计机器学习技术

• 批准号: 8204935
• 负责人: Montse Fuentes
• 金额: $ 29.25万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8204935
• 关键词: Algorithms; Behavior; Bioinformatics; Biological; Biological Markers; Biology; Biomedical Research; Classification; Clinical; Collaborations; Complex; DNA Microarray Chip; Data; Data Analyses; Data Set; Development; Engineering; Face; Family; Gene Expression; Genes; Genomics; Goals; Graph; Human; Learning; Machine Learning; Malignant Neoplasms; Medicine; Methods; Microarray Analysis; Modeling; Morphologic artifacts; Outcome; Pathway interactions; Phenotype; Play; Research; Role; Sampling; Scientist; Statistical Methods; Statistical Models; Structure; Techniques; Technology; Testing; The Cancer Genome Atlas; UNC Lineberger Comprehensive Cancer Center; Validation; Variant; Work; anticancer research; base; cancer genetics; cancer genomics; flexibility; insight; novel; novel strategies; practical application; predictive modeling; public health relevance; skills; statistics; success; tool; tumor

DESCRIPTION (provided by applicant): Gene expression provides a snapshot of the cellular changes that promote tumor malignancy. Quantitative gene expression analysis, especially as implemented by DNA microarrays, has identified many new important cancer related genes and led to the development of new genomic-based clinical tests. For the quantitative aspect of gene expression analysis, many statistical methods have been used to study human tumors and to classify them into groups that can be used to predict clinical behavior. Despite progress, with the rapid advance of technology, massive and complex data are being generated in cancer research. Analyzing such data becomes more and more challenging. These challenges call for novel statistical learning methods, especially for high dimensional and noisy data. The goal of this project is to develop a host of new statistical learning techniques for solving complicated learning problems. In particular, this project develops (1) novel techniques to assess statistical significance of clustering for high dimensional data; (2) several novel predictive models including classification and regression which are expected to yield highly competitive accuracy and interpretability; (3) new methods for high dimensional biomarker/variable selection; (4) new approaches to estimate high dimensional covariance/precision matrix for biological network construction. These new developments are expected to allow scientists to analyze complex cancer genomic data with accurate prediction accuracy and increased interpretability. The research team will apply the proposed techniques to cancer research data analysis. The success of this project will be important in bridging statistical machine learning and cancer research. PUBLIC HEALTH RELEVANCE: This project aims to develop a host of new statistical learning techniques for solving complicated learning problems, especially for problems with high dimensional and noisy data such as gene expression data. These new techniques are expected to allow scientists to analyze complex cancer genomic data with accurate prediction accuracy and increased interpretability.

描述（由申请人提供）：基因表达提供了促进肿瘤恶性肿瘤的细胞变化的快照。定量基因表达分析，尤其是通过 DNA 微阵列实现的分析，已经鉴定出许多新的重要癌症相关基因，并导致了新的基于基因组的临床测试的发展。对于基因表达分析的定量方面，许多统计方法已用于研究人类肿瘤并将其分类为可用于预测临床行为的组。尽管取得了进步，但随着技术的快速进步，癌症研究中正在产生大量复杂的数据。分析此类数据变得越来越具有挑战性。这些挑战需要新颖的统计学习方法，特别是对于高维和噪声数据。该项目的目标是开发一系列新的统计学习技术来解决复杂的学习问题。特别是，该项目开发了（1）评估高维数据聚类统计显着性的新技术； (2) 几种新颖的预测模型，包括分类和回归，预计将产生极具竞争力的准确性和可解释性； (3)高维生物标志物/变量选择的新方法； (4)估计生物网络构建的高维协方差/精度矩阵的新方法。这些新进展预计将使科学家能够分析复杂的癌症基因组数据，并具有准确的预测准确性和更高的可解释性。研究小组将把所提出的技术应用于癌症研究数据分析。该项目的成功对于连接统计机器学习和癌症研究具有重要意义。 公共健康相关性：该项目旨在开发一系列新的统计学习技术来解决复杂的学习问题，特别是针对高维和噪声数据（例如基因表达数据）的问题。这些新技术预计将使科学家能够分析复杂的癌症基因组数据，并具有准确的预测准确性和更高的可解释性。