FET: Small: Methods and Algorithms for microRNA Sensing: Interdependency Discovery and Inverse Problems

FET：小型：microRNA 传感的方法和算法：相互依赖性发现和逆问题

• 批准号: 2007807
• 负责人: Faramarz Fekri
• 金额: $ 42.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007807&HistoricalAwards=false
• 关键词: FET; Small; Methods; Algorithms; microRNA

Recent studies have shown that a small group of microRNA (miRNA) often acts as (disease) biomarkers whose concentration changes from the normal state can be used as one of the most promising methods of detecting various diseases such as cancer, infection and heart diseases at early stages. Further, several efforts have been made to use miRNAs as a predictive tool in response to medical treatments, and as therapeutics themselves. Moreover, miRNA-driven signaling cascade plays a crucial role in the context of diseases, and its understanding remains a challenge. Several past works suggest that miRNAs in a group or cluster collaboratively control the regulatory patterns, especially when they share specific target messenger RNAs. All of these underline the importance of miRNA sensing and discovery of miRNA-to-miRNA interactions as a complex regulatory network in a large genome-wide scale. However, measuring 1000+ gene expression levels individually using sophisticated sequencing technologies renders such solutions very costly and intractable. The team of investigators will develop an integrated framework consisted of biosensors and machine-learning models for both the recovery of miRNAs concentrations and discovery of their interdependency structure in regulatory networks by analyzing measurements from a small group of low-cost biosensors. The research impacts are expected to be significant in several areas. 1. On technology and products: Understanding miRNA-miRNA interactions and being able to monitor miRNA expression levels is likely to contribute to the development of diagnostic tools for several types of cancer, cardiac damage, muscle damage and other muscle pathologies, diabetes, liver injury, and many infection diseases. By providing effective miRNA sensing and monitoring mechanisms, the research has potential to reduce the cost of health care. 2. On education and learning: (i) Training of graduate and undergraduate students, (ii) Broadening the participation of women and minorities in this field, (iii) Disseminating the research results, (iv) Providing internship opportunities for k-12 teachers, (v) Enhancing scientific and technological understanding by participation in and organizing multi-disciplinary conferences and workshops, (vi) Establishing collaborative efforts with both the industry and academia, and (vii) Possible technology transfer of the solutions developed for miRNA sensing. The proposed research aims at establishing an integrated framework consisted of a measurement system as a front end and machine-learning algorithms as a back end to achieve two high-level interrelated goals: (i) To develop the foundation for machine learning solutions that would analyze measurements from an array of small number of low-cost biosensors (whose design is guided by the proposed machine learning framework) and discover miRNA-to-miRNA interdependency structures in a large population of miRNAs (e.g., over 1000 miRNAs), (ii) To develop a framework for solving the inverse problem of recovering miRNAs' molecular concentration levels from a low dimensional measurement by the proposed sensor array, via leveraging miRNA-to-miRNA dependency structures. Although aimed at biology applications, the research will advance the theory and design principles in several fronts with a broad effect in many other applications. Specifically, (1) The research, for the first time, will investigate and develop the theory of learning the structure of probabilistic graphical models in both parametric and non-parametric scenarios under indirect low-dimensional observations. (2) It will also introduce a novel paradigm based on density evolution on graphs that can tap into the prior (dependency) structure of a high-dimensional signal to design and optimize a compressive measurement system for the high-dimensional signal recovery. (3) The proposed work will advance theory and algorithms for solving the inverse problem by taking into account certain structures in the high-dimensional signal (e.g., conditional independencies induced by graphical models, sparsity). (4) The research, for the first time, will lead to development of cheap, modular, and fast-acting array of biosensors for miRNA measurement whose design principle is integrated with and influenced by the data analytic counterpart.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.

近年来的研究表明，一小部分微小RNA（microRNA，miRNA）通常作为疾病的生物标志物，其浓度在正常状态下发生变化，可以作为早期检测癌症、感染和心脏病等多种疾病的最有前途的方法之一。此外，已经做出了一些努力来使用miRNA作为响应于医学治疗的预测工具，以及作为治疗剂本身。此外，miRNA驱动的信号级联在疾病背景下起着至关重要的作用，对其理解仍然是一个挑战。一些过去的工作表明，一组或一簇中的miRNA协同控制调控模式，特别是当它们共享特定的靶信使RNA时。所有这些都强调了miRNA传感和发现miRNA与miRNA相互作用作为大基因组范围内复杂调控网络的重要性。然而，使用复杂的测序技术单独测量1000多个基因表达水平使得此类解决方案成本高昂且棘手。研究小组将开发一个由生物传感器和机器学习模型组成的综合框架，通过分析一小组低成本生物传感器的测量结果，恢复miRNA浓度并发现其在调控网络中的相互依赖结构。研究的影响预计将在几个领域显着。1.关于技术和产品：了解miRNA-miRNA相互作用并能够监测miRNA表达水平可能有助于开发几种类型的癌症，心脏损伤，肌肉损伤和其他肌肉病变，糖尿病，肝损伤和许多感染疾病的诊断工具。通过提供有效的miRNA传感和监测机制，该研究有可能降低医疗保健成本。2.关于教育和学习：㈠培训研究生和本科生，㈡扩大妇女和少数民族在这一领域的参与，㈢传播研究成果，㈣为幼儿园至12年级教师提供实习机会，㈤通过参加和组织多学科会议和讲习班，加强对科学和技术的了解，㈥与工业界和学术界建立合作努力，以及（vii）为miRNA传感开发的解决方案的可能技术转让。拟议的研究旨在建立一个集成框架，包括作为前端的测量系统和作为后端的机器学习算法，以实现两个高级相互关联的目标：（i）为机器学习解决方案奠定基础，该解决方案将分析一系列少量低成本生物传感器的测量结果（其设计由所提出的机器学习框架指导）并在大量miRNA中发现miRNA与miRNA的相互依赖性结构（例如，超过1000个miRNA），（ii）开发用于解决通过所提出的传感器阵列从低维测量恢复miRNA的分子浓度水平的逆问题的框架，通过利用miRNA到miRNA依赖性结构。虽然针对生物学应用，但该研究将在多个方面推进理论和设计原则，并在许多其他应用中产生广泛影响。具体而言，（1）该研究将首次研究和发展间接低维观测下参数和非参数场景下概率图模型结构学习的理论。(2)它还将引入一种基于图上密度演化的新范式，该范式可以利用高维信号的先验（依赖性）结构来设计和优化用于高维信号恢复的压缩测量系统。(3)拟议的工作将通过考虑高维信号中的某些结构（例如，由图形模型引起的条件独立性、稀疏性）。(4)该研究将首次开发出廉价、模块化和快速反应的生物传感器阵列，用于测量miRNA，其设计原理与数据分析对应物相结合并受其影响。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A General Framework for the Design of Compressive Sensing using Density Evolution

使用密度演化设计压缩感知的通用框架

• DOI: 10.1109/itw48936.2021.9611415
• 发表时间: 2021
• 期刊: 2021 IEEE Information Theory Workshop (ITW
• 作者: Zhang, Hang;Abdi, Afshin;Fekri, Faramarz
• 通讯作者: Fekri, Faramarz

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning

• DOI: 10.48550/arxiv.2301.01849
• 发表时间: 2023-01
• 作者: Muralikrishnna G. Sethuraman;Romain Lopez;Ramkumar Veppathur Mohan;F. Fekri;Tommaso Biancalani;Jan-Christian Hutter

A General Compressive Sensing Construct Using Density Evolution

• DOI: 10.1109/tsp.2022.3216708
• 发表时间: 2022-04
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Hang Zhang;A. Abdi;F. Fekri