Analytical Approaches to Massive Data Computation with Applications to Genomics

描述(由申请人提供)：我们建议设计和测试具有良好数学基础的算法和统计构造，用于分析大规模、异质和噪声数据。我们专注于对算法性能的全面分析评估和对分析结果的严格统计保证。该项目将利用私人投资机构最近在癌症基因组学、数据分析和严格的数据挖掘技术方面的工作。这些工作是由特定的应用程序驱动的，而在当前的项目中，我们的目标是开发适用于广泛应用程序集的一般原则和技术。与目前主要用于数据挖掘和机器学习的启发式方法相比，拟议的研究具有变革性，其重点是对算法性能和输出不确定性的统计测量进行严格的分析评估。虽然我们不能期望对所有数据挖掘和机器学习技术进行全面的数学分析，但在这一方向上的任何进展都将对这一学科的可靠性和科学影响做出重大贡献。虽然我们的工作是由分子生物学数据推动的，但我们预计这些技术将对其他面临大量多变量数据分析挑战的科学社区有用。分子生物学为测试先进的数据分析技术提供了极好的数据来源：具体地说，DNA/RNA序列数据库正以超指数速度增长。这些数据通常很大，而且噪声很大，它包括允许对分析进行实验验证的基因和表型特征。其中一个这样的数据库是癌症基因组图谱(TCGA)，我们将使用它来对拟议的方法进行初步测试。相关性(见说明)：该项目将倡导以可靠的数学和统计概念为基础的负责任的数据分析方法。这种方法提高了循证医学以及大数据分析的其他政策和社会应用的有效性。这项拟议的工作将在人类和癌症基因组数据上进行测试，为国家优先领域之一的卫生信息技术做出贡献。

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.