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

最近的研究表明，一小群microRNA(MiRNA)经常作为(疾病)生物标志物，其浓度从正常状态的变化可以作为最有前途的方法之一，在早期检测各种疾病，如癌症、感染和心脏病。此外，已经做出了几项努力，将miRNAs用作药物治疗反应的预测工具，以及作为治疗本身。此外，miRNA驱动的信号级联在疾病背景下发挥着关键作用，对其理解仍然是一个挑战。过去的几项工作表明，一个组或簇中的miRNAs协同控制调控模式，特别是当它们共享特定的靶信使RNAs时。所有这些都强调了miRNA感知和发现miRNA到miRNA相互作用作为一个大规模基因组范围内的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，使这种解决方案变得非常昂贵和棘手。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，通过分析一小群低成本生物传感器的测量结果，恢复miRNAs浓度，并发现它们在监管网络中的相互依存结构。预计这项研究将在几个领域产生重大影响。1.关于技术和产品：了解miRNA-miRNA的相互作用并能够监测miRNA的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理、糖尿病、肝脏损伤和许多感染性疾病的诊断工具。通过提供有效的miRNA传感和监测机制，这项研究有可能降低医疗保健成本。2.关于教育和学习：(1)培训研究生和本科生，(2)扩大妇女和少数群体在这一领域的参与，(3)传播研究成果，(4)为K-12教师提供实习机会，(5)通过参加和组织多学科会议和讲习班增进对科学和技术的了解，(6)与工业界和学术界开展合作，以及(7)可能转让为miRNA检测开发的解决方案。所提议的研究旨在建立由作为前端的测量系统和作为后端的机器学习算法组成的集成框架，以实现两个高级相关目标：(I)开发机器学习解决方案的基础，所述机器学习解决方案将分析来自少量低成本生物传感器(其设计由所提议的机器学习框架指导)的测量结果，并在大量miRNAs(例如，超过1000个miRNAs)中发现miRNA到miRNA的相互依赖结构，(Ii)开发用于解决通过所提议的传感器阵列从低维测量中恢复miRNAs的分子浓度水平的逆问题的框架，通过利用miRNA到miRNA的依赖结构。尽管这项研究的目标是生物学应用，但它将在几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。具体地说，(1)本研究将首次探索和发展在间接低维观测下，在参数和非参数情景下学习概率图形模型结构的理论。(2)提出了一种基于图的密度进化的新范式，该范式可以利用高维信号的先验(依赖)结构来设计和优化高维信号恢复的压缩测量系统。(3)通过考虑高维信号中的某些结构(如图形模型引起的条件独立性、稀疏性)，提出了求解反问题的理论和算法。(4)这项研究将首次导致开发用于miRNA测量的廉价、模块化和快速反应的生物传感器阵列，其设计原则与数据分析同行相结合并受到其影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.