Collaborative Research: CIF: Medium: New Methods for Learning on Hypergraphs for Single-Cell Chromatin Data Analysis

合作研究：CIF：Medium：用于单细胞染色质数据分析的超图学习新方法

• 批准号: 1956384
• 负责人: Olgica Milenkovic
• 金额: $ 50.81万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956384&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; New

In higher-order organisms, long DNA strands are tightly packed in the cell nucleus to accommodate spatial constraints. As different parts of the DNA structure are continuously accessed and operated on by a machinery of functional molecules, the packing is highly dynamic and carefully controlled. During the process of coiling and uncoiling of the DNA and supporting protein structures, important physical interactions between different genomic regions take place. These so-called chromatin interactions regulate cellular functions and aid in responding to external biological signals. Capturing evolving multiway chromatin interaction patterns is therefore of crucial importance for understanding genetic regulatory mechanisms and genomic network modules. This project aims to advance platforms for measuring single-cell chromatin interactions and develop accompanying machine learning algorithms that enable efficient information extraction from the acquired data. The specific machine learning questions to be addressed include denoising and imputing multiway chromatin measurements, extracting dynamic chromatin community signatures via new hypergraph clustering methods and determining local and long-range interactions patterns through appropriate generalizations of PageRank methods. Furthermore, special attention will be placed on interpreting the findings within different biological contexts. To maximize the utility of the new algorithmic schemes, all relevant implementations supporting single-cell chromatin interaction data mining and analysis will be made readily available to the public.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.

在高阶生物体中，将长DNA链紧密地堆积在细胞核中，以适应空间约束。由于DNA结构的不同部分通过功能分子的机械连续访问和操作，因此包装具有高度动态性和精心控制的。在DNA的盘绕和解开过程和支持蛋白质结构的过程中，发生了不同基因组区域之间的重要物理相互作用。这些所谓的染色质相互作用调节细胞功能，并有助于响应外部生物学信号。因此，捕获不断发展的多路染色质相互作用模式对于理解遗传调节机制和基因组网络模块至关重要。该项目旨在推进平台，以测量单细胞染色质相互作用，并开发随附的机器学习算法，从而可以从获得的数据中提取有效的信息。要解决的特定机器学习问题包括降解和插入多路染色质测量，通过新的HyperGraph群集方法提取动态染色质社区特征，并通过适当的Pagerank方法来确定局部和远程相互作用模式。此外，将特别注意在不同的生物学环境中解释发现。为了最大程度地提高新算法方案的实用性，所有支持单细胞染色质相互作用数据挖掘和分析的相关实现将容易地向公众提供。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的，这是值得的。

项目成果

Unlearning Graph Classifiers with Limited Data Resources

• DOI: 10.1145/3543507.3583547
• 发表时间: 2022-11
• 期刊: Proceedings of the ACM Web Conference 2023
• 作者: Chao Pan;Eli Chien;O. Milenkovic