Collaborative Research: CISE-MSI: DP: III: Information Integration and Association Pattern Discovery in Precision Phenomics

这个项目解决了一类涉及松散耦合的异构域的机器学习问题。这些问题在各个领域都有重要的应用，从精准农业(植物基因型到表型)到精准医学(基因型与疾病的关联)到生态学(土壤水分-气候相互作用)。除了人工智能(AI)和机器学习方面的具体挑战外，同样重要和相关的挑战是使人才库多样化，使其具备应对这些问题的技术能力。该项目有三个具体目标：(1)开发在松散耦合领域中发现关联模式的方法；(2)进行信息集成、原型开发和对开发的方法进行评估；(3)在西弗吉尼亚州立大学(WVSU)进行机器学习/人工智能及其在精准农业中的应用方面的研究和教育能力建设。该项目包括两个案例研究。第一个是关于精确农业中的基因型到表型的问题，特别是关于重要的果树作物哈巴内罗辣椒的果实表型。第二个将把所提出的学习模型应用于将水果的表型映射到感官知觉的问题上，这是预测水果作物的味道和味道所必需的。该项目还包括能力建设努力，以提高西弗吉尼亚州立大学教职员工的研究能力，同时为学生提供研究培训。教育活动包括为本科生和研究生开发数据素养课程以及机器学习和数据分析方面的研究培训。此外，该项目将教育教师、学生和当地地区的公众了解人工智能(AI)及其应用的创新和趋势。该项目包括精心设计的计划，让西弗吉尼亚州立大学的少数族裔学生和教职员工参与进来。西弗吉尼亚州立大学是一所HBCU(历史上一直是黑人学院和大学)，西弗吉尼亚大学(WVU)是一所R1院校。通过这样做，该项目将提高西弗吉尼亚州立大学整个大学的数据科学素养，并培养更多学生在数据驱动分析方面的技能。在数据科学和机器学习的基础上，识别看似完全不同的领域之间的可靠关联模式仍然是一个核心挑战。当涉及的每个领域都是复杂的、异质的并且拥有巨大的数据集时，要做到这一点是一项更加艰巨的任务。该项目确定了一类涉及松散耦合的异构域的问题，并提出了一个跨此类复杂域执行大规模关联发现的创新框架。我们的框架建立在强大的理论基础上，即植根于信息论的信息瓶颈。该项目通过开发新的方法来学习两个不同领域的隐藏关联，并整合这些领域的信息，从而为数据科学和机器学习的基础做出了新的理论贡献。该项目还开发了新的学习方法在精准农业和作物表型组学中的两个具体问题上的新应用。所提出的在所标识的松散耦合域类下发现关联模式的方法可以很容易地针对所标识的类中的任何成员进行修改。这一类相当普遍，包括人类努力的各个领域的问题，从农业到精密医学再到生态。该项目由CEISE微星研究扩展计划和既定的激励竞争研究计划(EPSCoR)联合资助。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。

This project addresses a general class of machine learning problems involving loosely-coupled heterogeneous domains. Such problems have significant applications in various fields, from precision agriculture (plant genotype-to-phenotype) to precision medicine (genotype-disease associations) to ecology (soil moisture-climate interactions). Beyond the specific challenges in artificial intelligence (AI) and machine learning, an equally important and related challenge is diversifying the talent pool with the technical capability to these problems. The project has three specific goals: (1) develop methods for association pattern discovery in loosely-coupled domains; (2) perform information integration, prototype development, and evaluation of the methods developed; (3) capacity building in research and education in machine learning/artificial intelligence and their applications in precision agriculture at West Virginia State University (WVSU). The project includes two case studies. The first is on the genotype-to-phenotype problem in precision agriculture, specifically on fruit phenotypes in habanero pepper, an important fruit crop. The second will apply proposed learning models to the problem of mapping fruit phenotype to sensory perception, which is needed to predict taste and flavor in fruit crops. The project also involves capacity building efforts to improve research capability for faculty at WVSU, while providing research training for students. Educational activities include curriculum development in data literacy and research training in machine learning and data analytics for undergraduate and graduate students. Further, the project will educate faculty, students, and the public within the local region on innovations and trends in artificial intelligence (AI) and its applications. The project includes deliberate plans to involve minority students and faculty from WVSU, an HBCU (Historically Black College and University) with collaborators at West Virginia University (WVU), an R1 institution. By so doing, the project will improve data science literacy across the entire university at WVSU and to develop more of the students’ skills in data-driven analysis. Identifying reliable patterns of association between seemingly disparate domains remains a core challenge at the foundation of data science and machine learning. Doing this when the domains involved are each complex, heterogeneous, and have enormous datasets is an even more arduous task. The project identifies a class of problems involving loosely-coupled heterogeneous domains, and propose an innovative framework for performing large-scale association discoveries across such complex domains. Our framework is built on a strong theoretical foundation, namely, the information bottleneck rooted in information theory. The project makes new theoretical contributions to the foundation of data science and machine learning by developing new methods for learning hidden associations across two heterogeneous domains, and for integrating information across such domains. The project also develops novel applications of the new learning methods to two specific problems in precision agriculture and crop phenomics. The proposed method of discovering association patterns under the identified class of loosely-couple domains can easily be modified for any member of this identified class. This class is quite general, and includes problems in various fields of human endeavor, from agriculture to precision medicine to ecology.This project is jointly funded by the CISE MSI Research Expansion Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.