RI: Small: Collaborative Research: New Directions in Spectral Learning with Applications to Comparative Epigenomics

RI：小型：协作研究：光谱学习的新方向及其在比较表观基因组学中的应用

基本信息

批准号：
1617157
负责人：
Kamalika Chaudhuri
金额：
$ 27.3万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617157&HistoricalAwards=false
关键词：
RI Small Collaborative Research New

项目摘要

The goal of this project is to design algorithms and statistical tools to build complex probabilistic models from massive quantities of data in a computationally efficient manner. This work is motivated by an important current problem in genomics, namely comparative epigenetics. While every cell in an organism has the same DNA sequence, epigenetic marks on the genome are known to be highly correlated with variation between cells. A pressing question in biology is to compare the epigenetic marks across different cell types to understand these differences. While massive amounts of data has been generated for this purpose, there is a great need for computational tools that can operate on this data and provide biologically meaningful solutions. This work will thus advance the state-of-the-art in the analysis of large complex data sets and advance the field of epigenomics. The broader impact of the work includes organizing workshops and tutorials at machine learning and bioinformatics venues, involving undergraduate students in research, and releasing open source software for the community. Specifically, this project will focus on spectral learning, which has recently provided principled and computationally efficient methods for learning parameters of probabilistic graphical models. While spectral learning methods are known for some simple latent variable models, a major barrier to realizing the potential of spectral learning in real-world applications is the lack of associated statistical tools such as regularization and hypothesis testing that connect these methods in a principled manner to end-to-end application frameworks. This project proposes to develop such statistical tools by integrating modern spectral learning with the classical statistical literature in econometrics on Generalized Method of Moments. The project proposes to formulate the statistical generalized method of moment procedures for complex graphical models in the context of spectral learning as constrained optimization problems and proposes ways of solving these problems. Finally, the novel algorithms developed will be directly applied to model epigenomics data sets from the ENCODE and Roadmap Epigenomics Projects to yield methods that can operate on the massive quantities of data and provide biologically meaningful solutions. These algorithms and software have the potential to have a widespread impact on the understanding of complex human diseases such as cancer and mental disorders. This will provide a basis for designing therapeutics for these diseases and advance society towards a future of Personalized Medicine.

该项目的目的是设计算法和统计工具，以计算有效的方式从大量数据中构建复杂的概率模型。这项工作是由基因组学当前重要的问题（即比较表观遗传学）引起的。尽管生物体中的每个细胞都具有相同的DNA序列，但已知基因组上的表观遗传标记与细胞之间的变异高度相关。生物学中的一个紧迫问题是比较不同细胞类型的表观遗传标记，以了解这些差异。尽管为此目的生成了大量数据，但对于可以在此数据上运行并提供具有生物学意义的解决方案的计算工具非常需要。因此，这项工作将推进大型复杂数据集分析并推进表观基因组学领域的最新作品。这项工作的更广泛影响包括在机器学习和生物信息学场所组织研讨会和教程，涉及研究生研究，并为社区发布开源软件。具体而言，该项目将集中在光谱学习上，光谱学习最近为概率图形模型的学习参数提供了原则性和计算有效的方法。虽然光谱学习方法是一些简单的潜在变量模型已知的，但在现实世界应用中实现光谱学习潜力的主要障碍是缺乏相关的统计工具，例如正则化和假设测试，这些工具和假设测试以原则上的方式将这些方法连接到端到端应用程序框架。该项目提议通过将现代光谱学习与经典的瞬间方法中的经典统计文献相结合，以开发此类统计工具。该项目建议在光谱学习的背景下为复杂的图形模型制定统计通用方法，作为受限的优化问题，并提出了解决这些问题的方法。最后，开发的新型算法将直接应用于模型的表观基因组学数据集，从编码和路线图表观基因组学项目中，以产生可以根据大量数据运行并提供具有生物学意义的解决方案的方法。这些算法和软件有可能对对复杂人类疾病（如癌症和精神疾病）的理解产生广泛影响。这将为这些疾病设计治疗学并迈向个性化医学的未来提供基础。