CAREER: Statistical and Computational Tools for the Analysis of High Dimensional Genetic Data

提案ID：DMS-0239427PI：Chiara SabattiTablle：Career：用于分析高维遗传数据的统计和计算工具摘要该项目将能够创建新的统计模型和计算工具，用于分析高维空间中的数据，就像遗传学领域产生的那样。特别是，调查员和她的同事将：(A)开发基因组序列模型，目的是确定结合位点的总数、它们的位置及其相互作用；(B)对基因阵列数据进行去噪，对各种基因的表达之间的相关性进行建模，并确定引起表达变化的不同化学信号的数目；(C)对“单倍型块”的概念进行建模，确定识别它们的程序，以便根据基因图谱的目的确定它们，并为同一背景下的多重比较制定适当的校正程序。该项目阐明了模型选择、多重比较、高维函数估计等主题之间的关系，并导致对贝叶斯模型、最小描述长度原则和错误发现率之间的联系有了更深的理解。拟议的研究还将开发一套新的计算工具，这些工具基于马尔科夫链蒙特卡罗抽样和在各种不同尺度上表示目标分布。概述的研究有助于解决与基因的作用和表达有关的一些基本问题，从而通过发现与疾病相关的基因、开发基因疗法以及在工业规模上过量生产感兴趣的蛋白质来改善整体福利。通过公开提供基因组和基因表达分析的算法，并升级计算基础设施，该项目扩大了对服务不足社区的科学调查的参与，并加强了研究的一般基础设施。拟议组织的跨学科讲习班、研究活动和课程确保广泛传播成果，以增进科学理解。为高中和大学教师组织跨学科教学统计研讨会的方向是将研究与教育相结合，促进教学、培训和学习。

Proposal ID: DMS-0239427PI: Chiara SabattiTitle: CAREER: Statistical and computational tools for the analysis of high dimensional genetic dataAbstractThis project will enable the creation of novel statistical models and computational tools for the analysis of data in high dimensional spaces, as the one generated in the field of genetics. In particular the investigator and her colleagues will (a) develop models for genomic sequences that aim at establishing the total number of binding sites, their location and their interaction with each other; (b) pursue de-noising of gene array data, modeling of the dependence between the expression of various genes, and the identification of the number of different chemical signals originating change in expression; (c) model the notion of ``haplotype blocks'' and define the procedures to identify them with the purpose of gene mapping, and develop appropriate procedures of correction for multiple comparison in the same context. The project illustrates relations between the topics of model selection, multiple comparison, high-dimensional function estimation and leads to deeper understanding of connections between Bayesian models, minimum description length principle, and false discovery rates. The proposed research will additionally develop a new set of computational tools that are based on Markov Chain Monte Carlo sampling and representation of the objective distribution on a variety of different scales. The outlined research helps to tackle some fundamental questions regarding the role and the expression of genes, thus leading to improvements of the general welfare, trough the discovery of genes related to diseases, the development of genetic therapies, and the engineering of the over-production of protein of interests on industrial scale. By making the algorithms for genome and gene expression analysis publicly available, and upgrading the computing infrastructure, the project broadens the participation to scientific investigation of under-served community and enhance the general infrastructure for research. The proposed organization of interdisciplinary workshops, research activities and courses assures a broad dissemination of the results to enhance scientific understanding. The organizations of seminars on teaching statistics in interdisciplinary settings for high-school and college instructors goes in the direction of integrating research and education, promoting teaching, training, and learning.