Analytical Approaches to Massive Data Computation with Applications to Genomics

海量数据计算的分析方法及其在基因组学中的应用

• 批准号: 8825472
• 负责人: Benjamin Raphael
• 金额: $ 7.13万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-18 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8825472
• 关键词: Advocate; Algorithms; Area; Big Data; Communities; DNA; Data; Data Analyses; Data Sources; Databases; Discipline; Effectiveness; Evaluation; Evidence Based Medicine; Genomics; Genotype; Health; Human Genome; Instruction; Machine Learning; Measures; Molecular Biology; Output; Performance; Phenotype; RNA Sequences; Research; Social Policies; Techniques; Testing; The Cancer Genome Atlas; Uncertainty; Validation; Work; base; cancer genome; cancer genomics; data mining; design; heuristics; mathematical analysis; transcriptome sequencing

DESCRIPTION (provided by applicant): We propose to design and test mathematically well founded algorithmic and statistical tectonics for analyzing large scale, heterogeneous and noisy data. We focus on fully analytical evaluation of algorithms' performance and rigorous statistical guarantees on the analysis results. This project will leverage on the PIs' recent work on cancer genomics data analysis and rigorous data mining techniques. Those works were driven by specific applications, while in the current project we aim at developing general principles and techniques that will apply to a broad sets of applications. The proposed research is transformative in its emphasis on rigorous analytical evaluation of algorithms' performance and statistical measures of output uncertainty, in contrast to the primarily heuristic approaches currently used in data ming and machine learning. While we cannot expect full mathematical analysis of all data mining and machine learning techniques, any progress in that direction will have significant contribution to the reliability and scientific impact of this discipline. While ou work is motivated by molecular biology data, we expect the techniques to be useful for other scientific communities with massive multi-variate data analysis challenges. Molecular biology provides an excellent source of data for testing advance data analysis techniques: specifically, DNA/RNA sequence data repositories are growing at a super-exponential rate. The data is typically large and noisy, and it includes both genotype and phenotype features that permit experimental validation of the analysis. One such data repository is The Cancer Genome Atlas (TCGA), which we will use for initial testing of the proposed approaches. RELEVANCE (See instructions): This project will advocate a responsible approach to data analysis, based on well-founded mathematical and Statistical concepts. Such an approach enhances the effectiveness of evidence based medicine and other policy and social applications of big data analysis. The proposed work will be tested on human and cancer genome data, contributing to health IT, one of the National Priority Domain Areas.

描述（由申请人提供）：我们建议设计和测试数学上良好的算法和统计构件，以分析大规模，异质和嘈杂数据。我们专注于对算法的性能和严格统计保证的全面分析评估。该项目将利用PI的最新研究癌症基因组学数据分析和严格的数据挖掘技术。这些作品是由特定应用程序驱动的，而在当前项目中，我们旨在开发适用于广泛应用程序的一般原则和技术。与目前在数据MING和机器学习中目前使用的主要启发式方法相比，该研究的重点是对算法的性能和输出不确定性的统计测量的严格分析评估的变革性。尽管我们不能期望对所有数据挖掘和机器学习技术进行全面数学分析，但该方向上的任何进展都将对该学科的可靠性和科学影响产生重大贡献。虽然OU工作是由分子生物学数据激励的，但我们希望这些技术对具有大量多变量数据分析挑战的其他科学界有用。分子生物学为测试预先数据分析技术提供了极好的数据来源：具体来说，DNA/RNA序列数据存储库的增长率正在以超指定速率增长。数据通常很大且嘈杂，并且包括允许对分析实验验证的基因型和表型特征。这样的数据存储库之一是癌症基因组图集（TCGA），我们将用于初步测试所提出的方法。相关性（请参阅说明）：该项目将基于基于基础的数学和统计概念提倡一种负责任的数据分析方法。这种方法增强了基于证据的医学以及大数据分析的其他政策和社会应用的有效性。拟议的工作将对人类和癌症基因组数据进行测试，并有助于健康，这是国家优先领域之一。