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

该项目解决了一类涉及松散耦合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-疾病关联）再到生态学（土壤水分-气候相互作用）。除了人工智能（AI）和机器学习的具体挑战之外，一个同样重要和相关的挑战是使具有解决这些问题的技术能力的人才库多样化。该项目有三个具体目标：（1）在松散耦合的领域开发关联模式发现的方法;（2）进行信息集成，原型开发和评估开发的方法;（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应用的研究和教育能力建设。该项目包括两个案例研究。第一个是关于精准农业中的基因型到表型问题，特别是关于重要水果作物哈瓦那辣椒的果实表型。第二个将建议的学习模型映射水果表型的感官知觉，这是需要预测水果作物的味道和风味的问题。该项目还涉及能力建设工作，以提高教师在西弗吉尼亚州立大学的研究能力，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研究培训。此外，该项目将教育教师，学生和公众在当地的创新和趋势，人工智能（AI）及其应用。该项目包括深思熟虑的计划，涉及少数民族学生和教师从WVSU，HBCU（历史上的黑人学院和大学）与合作者在西弗吉尼亚大学（WVU），一个R1机构。通过这样做，该项目将提高整个WVSU大学的数据科学素养，并培养学生在数据驱动分析方面的更多技能。识别看似不同的领域之间的可靠关联模式仍然是数据科学和机器学习基础的核心挑战。当所涉及的领域都是复杂的、异构的并且具有巨大的数据集时，这样做是一项更加艰巨的任务。该项目确定了一类问题，涉及松散耦合的异构域，并提出了一个创新的框架，执行大规模的关联发现在这样复杂的领域。我们的框架是建立在一个强大的理论基础上，即信息瓶颈植根于信息理论。该项目为数据科学和机器学习的基础做出了新的理论贡献，开发了新的方法来学习两个异构领域之间的隐藏关联，并整合这些领域的信息。该项目还开发了新学习方法在精准农业和作物表型组学两个具体问题上的新应用。所提出的方法发现下的松散耦合域的识别类的关联模式，可以很容易地修改为任何成员，这个识别类。该项目由CISE MSI研究扩展计划和激励竞争研究既定计划（EPSCoR）共同资助，反映了NSF的法定使命，通过使用基金会的智力价值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.