Collaborative Research: Computational Topology and Categorification of Cancer Genomic Data: Theory and Algorithms

合作研究：癌症基因组数据的计算拓扑和分类：理论和算法

基本信息

批准号：
1854770
负责人：
Javier Arsuaga
金额：
$ 34万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854770&HistoricalAwards=false
关键词：
Collaborative Research Computational Topology Categorification

项目摘要

Society is generating data at an unprecedented rate, currently estimated at 2.5 quintillion bytes daily. Many of these data sets are notably complex, particularly because they often involve interdependencies which are difficult to identify. In the field of cancer genomics, thousands of measurements can be obtained with the objective of discovering molecular signatures that characterize biological processes. However, advances in this area have been limited due to major computational challenges involved in identifying the structures that are present in both healthy and cancerous cells. This project aims to develop new topological methods to detect hidden dependencies within and across different types of data obtained from breast cancer patients. The project will intensively train three graduate students each year in these novel methods and expand the undergraduate and graduate curricula in data analysis and applied topology. Results and materials will be broadly disseminated to the scientific community through publications in open access and standard journals, conference presentations, and open source software. Results will be also shared with the public, including teachers and students in grades 10th to 12th, through training courses and art exhibits. Genomic technologies have revolutionized the field of genetics over the past decade, providing new methods for identifying thousands of genetic/molecular signals associated to specific phenotypes. Among these methods, Genome Wide Association Studies have accelerated the identification of specific genetic elements by testing thousands of genetic loci simultaneously. These approaches, however, are less useful for identifying co-occurrences of and interactions among genetic elements, conditions that appear to be ubiquitous in living organisms. To address this gap, the PIs will develop new mathematical methods to enable the identification of interactions among genetic elements in cancer, thereby testing the hypothesis that many cancer phenotypes are regulated by co-occurring genetic events. Using the combined tools of modern topological and data analyses, including machine learning techniques, the research team will identify such co-occurrences by: analyzing generators of homology groups, implementing a computational data-driven theory of fiber bundles, and developing new models of cancer evolution using Khovanov-type categorification methods. The ultimate goal of this project is to develop new computational tools in time series analysis that help identify hidden interdependencies of data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社会正在以前所未有的速度生成数据，目前估计每天产生 2.5 万亿字节。其中许多数据集非常复杂，特别是因为它们通常涉及难以识别的相互依赖性。在癌症基因组学领域，可以获得数千种测量结果，目的是发现表征生物过程的分子特征。然而，由于识别健康细胞和癌细胞中存在的结构所涉及的重大计算挑战，该领域的进展受到限制。该项目旨在开发新的拓扑方法来检测从乳腺癌患者获得的不同类型数据内部和之间隐藏的依赖性。该项目每年将集中培训三名研究生使用这些新颖的方法，并扩展数据分析和应用拓扑方面的本科生和研究生课程。结果和材料将通过开放获取和标准期刊上的出版物、会议演示和开源软件广泛传播给科学界。成果还将通过培训课程和艺术展览与公众分享，包括 10 至 12 年级的教师和学生。过去十年，基因组技术彻底改变了遗传学领域，提供了识别与特定表型相关的数千种遗传/分子信号的新方法。在这些方法中，全基因组关联研究通过同时测试数千个遗传位点，加速了特定遗传元件的鉴定。然而，这些方法对于识别遗传元件的共现和相互作用不太有用，这些遗传元件似乎在生物体中普遍存在。为了解决这一差距，PI 将开发新的数学方法，以识别癌症遗传因素之间的相互作用，从而检验许多癌症表型受到同时发生的遗传事件调节的假设。使用现代拓扑和数据分析的组合工具，包括机器学习技术，研究团队将通过以下方式识别此类共现：分析同源群的生成器、实施计算数据驱动的纤维束理论以及开发新的癌症模型使用 Khovanov 型分类方法进行进化。该项目的最终目标是在时间序列分析中开发新的计算工具，帮助识别数据隐藏的相互依赖性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。